frontend去ai化

改readme

.claude/settings.json

@@ -0,0 +1,9 @@
+{
+  "permissions": {
+    "allow": [
+      "Bash(git checkout *)",
+      "Bash(git add *)",
+      "Bash(git commit -m ' *)"
+    ]
+  }
+}

.dockerignore

@@ -0,0 +1,18 @@
+# 前端
+frontend/dist
+frontend/node_modules
+
+# 后端
+backend/.venv
+backend/.git
+backend/__pycache__
+backend/*.pyc
+backend/*.pyo
+backend/*.pyd
+backend/.pytest_cache
+backend/.mypy_cache
+backend/.cache
+backend/.env
+backend/*.log
+backend/dist
+backend/build

.github/workflows/docker-image.yml

@@ -1,35 +0,0 @@
-name: Docker Image CI
-
-on:
-  push:
-    tags: ['v*']
-
-jobs:
-  build:
-    runs-on: ubuntu-latest
-
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-
-      - name: Logout Docker (avoid wrong credentials)
-        run: docker logout || true
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-
-      - name: Install the latest version of uv
-        uses: astral-sh/setup-uv@v7
-
-      - name: uv lock
-        working-directory: backend
-        run: uv lock
-      
-      - name: Build Docker Image (with BuildKit)
-        working-directory: backend
-        run: |
-          docker build \
-            --progress=plain \
-            -t insightradar-backend:${{ github.ref_name }} \
-            -t insightradar-backend:latest \
-            .

.gitignore

@@ -37,13 +37,12 @@ MANIFEST
 *.manifest
 *.spec
 
-# uv
-*.lock
-
 # Installer logs
 pip-log.txt
 pip-delete-this-directory.txt
 
+**/logs/*
+
 # Unit test / coverage reports
 htmlcov/
 .tox/
@@ -190,3 +189,8 @@ cython_debug/
 
 **/data/*
 **/docker/*
+backend/app/static/*
+
+test*.*
+
+docs/**

CLAUDE.md

@@ -0,0 +1,108 @@
+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## 项目简介
+
+InsightRadar（聚势智见）是一个热点资讯聚合平台。核心流程：定时爬取微博、知乎、百度等平台热搜 → 用本地 Embedding 模型（Qwen3-Embedding-4B）做余弦相似度语义聚类 → 合并为 `UnifiedEvent`（大事件）→ 调用 DeepSeek 等大模型生成 AI 摘要与标签 → 按用户订阅关键词定时推送邮件简报。
+
+## 开发命令
+
+### 后端（Python / FastAPI）
+
+```bash
+cd backend
+uv sync                            # 安装依赖
+uv run python main.py              # 启动开发服务器（默认 :8000）
+# 或
+uv run uvicorn app.main:app --reload --port 8000
+```
+
+### 前端（Vue 3 / Vite）
+
+```bash
+cd frontend
+npm install
+npm run dev          # 开发服务器（Vite，默认 :5173，代理到后端）
+npm run build        # 构建产物到 dist/
+npm run type-check   # TypeScript 类型检查
+npm run lint         # oxlint + eslint 双重 lint（自动修复）
+npm run format       # Prettier 格式化 src/
+```
+
+### 生产部署（将前端打包集成到后端）
+
+```bash
+cd frontend && npm run build
+cp -r dist/* ../backend/app/static/
+```
+
+## 架构概览
+
+### 后端分层
+
+```
+backend/app/
+├── main.py          # FastAPI 入口，APScheduler 调度（抓取/摘要/推送三个定时任务）
+├── database.py      # SQLAlchemy engine（SQLite WAL 模式，支持 SQLALCHEMY_DATABASE_URL 切换）
+├── initialize.py    # 启动时幂等写入默认信息源（今日头条、微博等11个平台）
+├── models/models.py # 全部 ORM 表定义（单文件）
+├── api/
+│   ├── router.py    # 统一挂载所有子路由，前缀 /api/v1
+│   └── endpoints/   # auth / events / preferences / delivery / revisions / sources / stats
+├── services/
+│   ├── fetcher_service.py   # 爬取热搜 + Embedding 生成 + 语义聚类入库（核心）
+│   ├── summary_service.py   # 调用大模型生成 AI 摘要与标签
+│   ├── matching_service.py  # 精确 + 语义双模式匹配用户兴趣
+│   └── delivery_service.py  # 检查推送时间窗口并发送邮件简报
+├── core/
+│   ├── security.py          # JWT 签发与校验
+│   └── verification/        # 验证码逻辑（Redis 或 DB 双模式存储）
+├── crud/            # 数据库 CRUD 操作
+├── schemas/         # Pydantic 请求/响应 Schema
+├── prompts/         # LLM Prompt 模板
+└── static/          # 前端构建产物（生产环境）
+```
+
+### 前端分层
+
+```
+frontend/src/
+├── api/             # 封装 fetch 请求（基于 config/apiBase.ts，前缀 /api/v1）
+├── stores/          # Pinia 状态（auth / theme）
+├── router/          # Vue Router（requiresAuth / guestOnly meta 守卫）
+├── views/           # 页面：Dashboard / Search / Topics / Delivery / Revisions / Login / Register
+├── layouts/         # DashboardLayout（统一侧边栏）
+└── components/      # 通用组件（UnifiedEventCard 等）
+```
+
+### 关键数据模型
+
+- `UnifiedEvent`：语义聚类后的"大事件"，含 AI 摘要、`center_embedding`（聚类中心向量）、`hot_score`
+- `TrendingEvent`：各平台原始热搜，通过 `external_id`（MD5 指纹）去重，`unified_event_id` 关联大事件
+- `ExtractedTopic` / `DiscussionComment`：多态设计，`target_type` 区分挂载在 EVENT / TREND / ARTICLE 下
+- `DeliveryHistory`：防重推记录，唯一约束 `(user_id, target_type, target_id)`
+
+### Embedding 模型
+
+`fetcher_service.py` 在模块级加载 `SentenceTransformer` 全局单例（`embedder_model`）。`matching_service.py` 直接 import 复用该单例，避免重复加载。模型路径由 `EMBEDDING_MODEL_PATH` 配置，需提前将模型文件放入 `backend/data/` 目录。
+
+## 配置
+
+`.env` 文件放在项目根目录（或 `backend/data/`，两处均可），关键变量：
+
+| 变量 | 说明 |
+|------|------|
+| `SQLALCHEMY_DATABASE_URL` | 默认 `sqlite:///./data/demo.db`，可切换 PostgreSQL |
+| `EMBEDDING_MODEL_PATH` | 本地 Embedding 模型路径 |
+| `AI_API_KEY` | 大模型 API Key（DeepSeek 等 OpenAI 兼容接口） |
+| `SIMILARITY_THRESHOLD` | 热搜语义聚类阈值（默认 0.72） |
+| `AUTH_CODE_STORE` | 验证码存储模式：`db`（无 Redis 时）或 `redis` |
+| `REDIS_URL` | Redis 连接，为空时验证码自动回退到数据库 |
+
+## 注意事项
+
+- **后端工作目录**：必须在 `backend/` 下运行，静态文件路径 `app/static` 是相对路径
+- **Embedding 模型冷启动慢**：首次加载 Qwen3-Embedding-4B 约需数十秒，是正常现象
+- **前端 API 路径**：所有请求统一经 `src/config/apiBase.ts` 的 `fetchApi()` 发出，前缀 `/api/v1`，无需手动拼接
+- **数据库迁移**：当前使用 `Base.metadata.create_all()` 自动建表，不使用 Alembic；修改 Model 字段后需手动处理已有数据库

README.md

@@ -1,2 +1,70 @@
-# InsightRadar
+# 聚势智见 — 基于语义聚类与大模型的热点资讯聚合平台
-An AI-powered trend monitoring and news intelligence platform
+
+一个智能热点监测与个性化分发平台，通过语义聚类与大模型技术，将分散在微博、知乎、抖音、百度等平台的热点资讯自动归并为统一事件，生成AI摘要与标签，并支持个性化订阅与定时推送。
+
+## 核心特性
+
+- **跨平台热点聚合**：基于Embedding语义相似度计算，自动识别不同平台的同一事件
+- **AI智能摘要**：调用大模型生成统一标题、综合摘要与标准化标签
+- **个性化推荐**：支持关键词订阅、语义匹配与多因子排序
+- **舆情分析工具**：提供热度趋势追踪、标题修改监控、时间线分析
+- **定时简报推送**：自定义推送时间与接收邮箱，生成个性化AI简报
+
+## 快速部署
+
+### 方式一：Docker部署（推荐）
+
+**环境要求**
+- Linux系统（推荐Ubuntu 22.04 LTS / Debian 12）
+- Docker ≥ 20.10.0，Docker Compose v2
+- 内存 ≥ 512MB（建议1GB以上）
+
+**部署步骤**
+
+```bash
+# 1. 构建镜像
+docker build -t insightradar:latest .
+
+# 2. 配置目录（参考docker/ereadm.txt）
+mkdir -p ./data ./logs
+
+# 3. 启动服务
+cd docker
+docker compose up -d
+```
+
+### 方式二：源码部署
+
+**环境要求**
+
+- Python ≥ 3.11，uv包管理器
+- Node.js ≥ 22
+- 内存 ≥ 512MB
+
+**后端部署**
+
+``` bash
+# 复制
+cd backend
+uv sync
+uv run
+```
+
+**前端部署**
+
+```bash
+# 复制
+cd frontend
+npm install
+npm run build
+# 将dist/目录内容复制到 backend/app/static/
+```
+
+**配置说明**
+
+- 复制 .env.example 为 .env 并填写配置
+- 将Embedding模型（Qwen3-Embedding-4B）放入 backend/data/ 目录
+
+### 访问应用
+
+部署完成后，通过 http://<服务器IP>:<配置端口> 访问Web界面。

backend/.dockerignore

@@ -1,13 +0,0 @@
-.venv
-.git
-__pycache__
-*.pyc
-*.pyo
-*.pyd
-.pytest_cache
-.mypy_cache
-.cache
-.env
-*.log
-dist
-build

backend/app/api/endpoints/auth.py

@@ -1,6 +1,3 @@
-"""
-认证模块：用户注册、登录、邮箱验证码（支持 Redis / 数据库双存储与自动降级）
-"""
 import json
 import math
 import os
@@ -69,7 +66,7 @@ def _normalize_email(email: str) -> str:
 def _build_verification_email(code: str, purpose_text: str, expire_minutes: int) -> str:
     return f"""
     <div style="font-family: Arial, sans-serif; line-height: 1.6; color: #222;">
-        <h2 style="margin-bottom: 12px;">InsightRadar 邮箱验证</h2>
+        <h2 style="margin-bottom: 12px;">聚势智见邮箱验证</h2>
         <p>您的{purpose_text}验证码是：</p>
         <p style="font-size: 28px; font-weight: bold; letter-spacing: 4px; color: #0b57d0;">{code}</p>
         <p>该验证码在 {expire_minutes} 分钟内有效。请勿泄露给他人。</p>
@@ -203,7 +200,7 @@ async def send_register_code(
 
         await send_html_email(
             to_email=email,
-            subject=f"【{code}】InsightRadar 注册验证码",
+            subject=f"【{code}】聚势智见 注册验证码",
             html_content=_build_verification_email(
                 code, "注册", REGISTER_CODE_EXPIRE_MINUTES
             ),
@@ -241,7 +238,7 @@ async def send_login_code(
 
         await send_html_email(
             to_email=email,
-            subject=f"【{code}】InsightRadar 登录验证码",
+            subject=f"【{code}】聚势智见 登录验证码",
             html_content=_build_verification_email(
                 code, "登录", LOGIN_CODE_EXPIRE_MINUTES
             ),

backend/app/api/endpoints/delivery.py

@@ -1,5 +1,3 @@
-# 推送设置 API：管理用户的推送时间表和推送渠道
-# 关键约束：同一用户两条推送时间间隔至少 30 分钟
 from datetime import time as dt_time
 from typing import List
 

backend/app/api/endpoints/events.py

@@ -1,7 +1,3 @@
-# app/api/endpoints/events.py
-"""
-事件模块：统一事件列表、详情、搜索时间线（支持精确/语义/混合匹配）
-"""
 import json
 import os
 import time
@@ -41,10 +37,8 @@ SEARCH_MAX_HOURS = int(os.getenv("SEARCH_MAX_HOURS", "168"))
 
 router = APIRouter()
 
-# 排名轨迹最多返回的点数，避免时间跨度过大时响应体过重。
 MAX_RANKING_POINTS = 30
 
-# 统一事件列表接口的短期缓存。
 _UNIFIED_EVENTS_CACHE: Dict[str, Tuple[float, PaginatedUnifiedEventResponse]] = {}
 CACHE_TTL_SECONDS = 60
 

backend/app/api/endpoints/preferences.py

@@ -1,6 +1,3 @@
-"""
-用户偏好模块：兴趣关键词的增删查、基于关键词的个性化事件推荐
-"""
 import time
 from typing import Any, Dict, List, Tuple
 
@@ -20,7 +17,6 @@ from app.services.matching_service import recommend_events_for_user
 
 router = APIRouter()
 
-# --- 轻量级接口缓存配置 ---
 _RECOMMEND_CACHE: Dict[str, Tuple[float, Any]] = {}
 CACHE_TTL_SECONDS = 60
 
@@ -29,7 +25,6 @@ def _invalidate_user_cache(user_id: int):
     keys_to_delete = [k for k in _RECOMMEND_CACHE.keys() if k.startswith(f"{user_id}:")]
     for k in keys_to_delete:
         _RECOMMEND_CACHE.pop(k, None)
-# ---------------------------
 
 def _ensure_self_access(path_user_id: int, current_user: AppUser) -> None:
     """校验路径 user_id 是否为当前登录用户本人。"""
@@ -93,7 +88,7 @@ def create_user_preference(
         )
 
     db.refresh(db_obj)
-    _invalidate_user_cache(user_id)  # 失效推荐缓存
+    _invalidate_user_cache(user_id)
     return db_obj
 
 
@@ -122,7 +117,7 @@ def delete_user_preference(
 
     db.delete(preference)
     db.commit()
-    _invalidate_user_cache(user_id)  # 失效推荐缓存
+    _invalidate_user_cache(user_id)
     return None
 
 
@@ -143,7 +138,6 @@ def recommend_events(
     """基于用户兴趣词推荐事件（精确匹配 + 语义匹配）。"""
     _ensure_self_access(user_id, current_user)
 
-    # 推荐结果缓存，避免频繁调用匹配服务
     cache_key = f"{user_id}:{min_hot}:{hours}:{limit}:{semantic_threshold}:{sort_by}"
     current_time = time.time()
     
@@ -151,7 +145,6 @@ def recommend_events(
         expire_time, cached_data = _RECOMMEND_CACHE[cache_key]
         if current_time < expire_time:
             return cached_data
-    # -----------------------
 
     matched = recommend_events_for_user(
         db,
@@ -189,10 +182,8 @@ def recommend_events(
 
     # 写入缓存，超过 2000 条时清空防止内存膨胀
     if len(_RECOMMEND_CACHE) > 2000:
-        # 防止内存无限增长
         _RECOMMEND_CACHE.clear()
         
     _RECOMMEND_CACHE[cache_key] = (current_time + CACHE_TTL_SECONDS, response)
-    # ------------------
 
     return response

backend/app/api/endpoints/revisions.py

@@ -1,4 +1,3 @@
-# 公关修改追踪 API：查询热搜标题被偷偷修改的历史记录，用于舆情监测
 from datetime import timedelta
 from typing import List, Optional
 
@@ -39,7 +38,6 @@ def list_headline_revisions(
     """
     time_limit = utcnow() - timedelta(hours=hours)
 
-    # 关联 TrendingEvent、InfoSource 获取平台名和链接
     rows = (
         db.query(HeadlineRevision, InfoSource.source_name, TrendingEvent.event_url)
         .join(TrendingEvent, HeadlineRevision.event_id == TrendingEvent.id)

backend/app/api/endpoints/sources.py

@@ -1,7 +1,3 @@
-# app/api/endpoints/sources.py
-"""
-信息源模块：信息源的增删改查，供爬虫与后台管理使用
-"""
 from fastapi import APIRouter, Depends, HTTPException, status
 from sqlalchemy.orm import Session
 from typing import List
@@ -43,6 +39,4 @@ async def update_info_source(source_id: int, source_in: InfoSourceUpdate, db: Se
     source = crud_source.get(db=db, source_id=source_id)
     if not source:
         raise HTTPException(status_code=404, detail="该信息源不存在")
-
-    # 直接把查出来的数据库对象和前端传来的 Pydantic 对象丢给 CRUD 处理
     return crud_source.update(db=db, db_obj=source, obj_in=source_in)

backend/app/api/endpoints/stats.py

@@ -1,4 +1,3 @@
-# 系统状态监控 API：返回爬虫集群运行概况（信息源数、今日抓取量、最近同步时间等）
 from datetime import datetime, timedelta
 from typing import Optional
 
@@ -28,7 +27,6 @@ def get_system_stats(db: Session = Depends(get_db)):
     """获取爬虫集群的当日运行状态。"""
     today_start = utcnow().replace(hour=0, minute=0, second=0, microsecond=0)
 
-    # 信息源统计：总数与启用数
     total_sources = db.query(func.count(InfoSource.id)).scalar() or 0
     active_sources = (
         db.query(func.count(InfoSource.id))
@@ -36,7 +34,6 @@ def get_system_stats(db: Session = Depends(get_db)):
         .scalar() or 0
     )
 
-    # 今日任务统计：抓取条数、成功/失败任务数
     today_tasks = (
         db.query(DataSyncTask)
         .filter(DataSyncTask.created_at >= today_start)
@@ -47,7 +44,6 @@ def get_system_stats(db: Session = Depends(get_db)):
     success_count = sum(1 for t in today_tasks if t.task_status == TaskStatus.SUCCESS)
     error_count = sum(1 for t in today_tasks if t.task_status == TaskStatus.ERROR)
 
-    # 最后一次同步时间
     last_task = (
         db.query(DataSyncTask)
         .filter(DataSyncTask.task_status == TaskStatus.SUCCESS)

backend/app/api/router.py

@@ -1,4 +1,3 @@
-# app/api/router.py
 from fastapi import APIRouter
 from app.api.endpoints import auth, delivery, events, preferences, revisions, sources, stats
 

backend/app/core/verification/email/RespositoryImpl/MemoryRepository.py

@@ -1,5 +1,3 @@
-# app/verification/backends/memory.py
-
 from functools import lru_cache
 import time
 import json

backend/app/crud/crud_source.py

@@ -1,7 +1,5 @@
-# app/crud/crud_source.py
+from sqlite3 import IntegrityError
-"""
+
-信息源 CRUD：对 InfoSource 的增删改查，供 API 与爬虫使用
-"""
 from sqlalchemy.orm import Session
 from typing import List, Optional
 
@@ -22,10 +20,20 @@ def get_multi(db: Session, skip: int = 0, limit: int = 100) -> List[InfoSource]:
 def create(db: Session, obj_in: InfoSourceCreate) -> InfoSource:
     """创建新的信息源"""
     db_obj = InfoSource(**obj_in.model_dump())
+    exits =db.query(InfoSource).filter(InfoSource.source_name == db_obj.source_name).first()
+    if exits:
+        db.close()
+        return db_obj
+    try:
         db.add(db_obj)
         db.commit()
         db.refresh(db_obj)
         return db_obj
+    except IntegrityError:
+        db.rollback()
+    finally:
+        db.close()
+        return db_obj
 
 
 def update(db: Session, db_obj: InfoSource, obj_in: InfoSourceUpdate) -> InfoSource:

backend/app/database.py

@@ -1,4 +1,4 @@
-# database.py
+# AI辅助生成：deepseek-v3-2，2026年3月20日
 import os
 
 from dotenv import load_dotenv

backend/app/initialize.py

@@ -1,16 +1,12 @@
-import requests
+from app.database import SessionLocal
-import json
+from app.crud.crud_source import create
+from app.models.models import SourceType
+from app.schemas.source_schema import InfoSourceCreate
 
-# 请将此处的 URL 替换为您实际的 API 基础域名
+# AI辅助生成：deepseek-v3-2，2026年3月20日
-api_url = "http://10.252.130.135:8000/api/v1/sources/"
 
-# 请求头
+def init():
-headers = {
-    "Content-Type": "application/json",
-    # "Authorization": "Bearer YOUR_TOKEN" # 如果接口需要鉴权，请取消注释并填入 Token
-}
 
-# 解析后的数据源列表
     sources_data = [
         {"name": "今日头条", "url": "toutiao"},
         {"name": "百度热搜", "url": "baidu"},
@@ -25,22 +21,17 @@ sources_data = [
         {"name": "知乎", "url": "zhihu"}
     ]
 
-# 遍历数据并发送 POST 请求
     for item in sources_data:
-    payload = {
-        "source_name": item["name"],
-        "source_type": "HOT_TREND",
-        "home_url": item["url"],
-        "is_enabled": True
-    }
-
         try:
-        response = requests.post(api_url, headers=headers, data=json.dumps(payload))
+            with SessionLocal() as db:
-        if response.status_code in (200, 201):
+
-            print(f"✅ 成功创建: {item['name']}")
+                create(db, InfoSourceCreate(
-        else:
+                    source_name=item["name"],
-            print(f"❌ 创建失败: {item['name']} - 状态码: {response.status_code} - 详情: {response.text}")
+                    source_type=SourceType.HOT_TREND,
+                    home_url=item["url"],
+                    is_enabled=True
+                ))
+                print(f"创建订阅源{item['name']}")
+
         except Exception as e:
             print(f"⚠️ 请求异常: {item['name']} - 错误: {e}")
-
-print("执行完毕！")

backend/app/main.py

@@ -1,8 +1,11 @@
-# app/main.py
+# AI辅助生成：deepseek-v3-2，2026年3月20日
 import logging
 import os
+from pathlib import Path
+from fastapi.responses import FileResponse, HTMLResponse, JSONResponse
+import httpx
 from contextlib import asynccontextmanager
-from fastapi import FastAPI
+from fastapi import FastAPI, HTTPException, Request, staticfiles
 from fastapi.middleware.cors import CORSMiddleware
 from dotenv import load_dotenv
 
@@ -21,20 +24,17 @@ from app.services.summary_service import generate_unified_summaries
 from app.services.delivery_service import check_and_deliver
 from app.database import engine
 from app.models.models import Base
+from app.initialize import init
 
 # 路由总线
 from app.api.router import api_router
 
-load_dotenv()
 CRAWL_INTERVAL = int(os.getenv("CRAWL_INTERVAL_MINUTES", 10))
 SUMMARY_INTERVAL = int(os.getenv("SUMMARY_INTERVAL_MINUTES", 30))
 
 scheduler = AsyncIOScheduler()
 
 
-# ==========================================
-# 1. 生命周期管理：App 启动时自动建表 & 启动调度器
-# ==========================================
 @asynccontextmanager
 async def lifespan(app: FastAPI):
     # 1. 数据库建表
@@ -42,7 +42,11 @@ async def lifespan(app: FastAPI):
     Base.metadata.create_all(bind=engine)
     logging.info("数据库表初始化完成！")
     
-    # 2. 配置并启动定时任务
+    logging.info("初始化订阅源")
+    init()
+    logging.info("订阅源初始化完毕")
+    
+    # 爬取订阅源
     scheduler.add_job(
         fetch_and_save_trending_data,
         'interval',
@@ -59,7 +63,7 @@ async def lifespan(app: FastAPI):
         id='ai_summary_job',
         replace_existing=True
     )
-    # 推送调度：每分钟检查是否有用户需要接收邮件推送
+    # 推送调度
     scheduler.add_job(
         check_and_deliver,
         'interval',
@@ -73,24 +77,14 @@ async def lifespan(app: FastAPI):
     logging.info(f"AI 摘要生成任务已启动，每 {SUMMARY_INTERVAL} 分钟执行一次")
     logging.info("邮件推送调度已启动，每分钟检查一次")
 
-    # 为了测试方便，启动时立即执行一次
+    yield
-    # await fetch_and_save_trending_data()
 
-    # await generate_unified_summaries()
-
-    yield  # 此时 FastAPI 开始接受请求
-
-    # 优雅关闭
     scheduler.shutdown()
     logging.info("定时任务已安全关闭")
 
 
-# 初始化 FastAPI
 app = FastAPI(title="AI 新闻聚合引擎 API", lifespan=lifespan)
 
-# ==========================================
-# 2. CORS 中间件：允许前端开发服务器跨域请求
-# ==========================================
 app.add_middleware(
     CORSMiddleware,
     # allow_origins=["http://localhost:5173", "http://127.0.0.1:5173"],
@@ -100,14 +94,26 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
-# ==========================================
-# 3. 挂载路由总线
-# ==========================================
-# 版本控制
 app.include_router(api_router, prefix="/api/v1")
 
+# AI辅助生成结束
+
+@app.get("/api/{full_path:path}")
+async def api_not_found(full_path: str):
+    return {"detail": "API Not Found"}
+
+staticPath = staticfiles.StaticFiles(directory="app/static", html=True)
+
+app.mount("/", staticPath, name="static")
+
+INDEX_HTML = Path("app/static/index.html").read_text(encoding="utf-8")
+
+@app.exception_handler(404)
+async def not_found_handler(request: Request, exc: HTTPException):
+    if request.url.path.startswith("/api/"):
+        return JSONResponse({"detail": "Not Found"}, status_code=404)
+    return HTMLResponse(INDEX_HTML)
 
-# 健康检查
 @app.get("/", tags=["健康检查"])
 async def root():
     return {"message": "Welcome to AI News Aggregator API", "status": "ok"}

backend/app/models/models.py

@@ -1,4 +1,3 @@
-# models.py
 from datetime import datetime, timezone, time
 from typing import Optional, Any
 import enum
@@ -9,11 +8,6 @@ from sqlalchemy import (
 )
 from sqlalchemy.orm import DeclarativeBase, Mapped, mapped_column, relationship
 
-
-# ==========================================
-# 0. 全局基类、枚举定义与动态类型
-# ==========================================
-
 class Base(DeclarativeBase):
     """
     SQLAlchemy 2.0 声明式基类
@@ -21,9 +15,6 @@ class Base(DeclarativeBase):
     """
     pass
 
-
-# 让代码在 SQLite 环境下自动降级为 Integer 以保证自增正常工作，
-# 而在生产环境部署到 PostgreSQL 或 MySQL 时，依然会使用容量更大的 BigInteger。
 BigIntType = BigInteger().with_variant(Integer, "sqlite")
 
 
@@ -70,10 +61,6 @@ def utcnow():
     """
     return datetime.now(timezone.utc)
 
-
-# ==========================================
-# 模块一：信息源管理
-# ==========================================
 class InfoSource(Base):
     """
     抓取源配置表
@@ -94,10 +81,10 @@ class InfoSource(Base):
     created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), default=utcnow)
     updated_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), default=utcnow, onupdate=utcnow)
     
+    __table_args__ = (
+        UniqueConstraint("source_name", name="uix_source_name"),
+    )
 
-# ==========================================
-# 模块二：AI 语义聚类中枢 (大事件池)
-# ==========================================
 class UnifiedEvent(Base):
     """
     AI 统一事件表 (核心大脑)
@@ -120,10 +107,6 @@ class UnifiedEvent(Base):
     created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), default=utcnow)
     updated_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), default=utcnow, onupdate=utcnow)
 
-
-# ==========================================
-# 模块三：内容存储库 (热搜 & 新闻子节点)
-# ==========================================
 class TrendingEvent(Base):
     """
     各平台热搜数据明细表
@@ -176,8 +159,7 @@ class NewsArticle(Base):
 
     id: Mapped[int] = mapped_column(BigIntType, primary_key=True, autoincrement=True)
     source_id: Mapped[int] = mapped_column(ForeignKey("info_sources.id"), comment="所属信息源ID")
-    unified_event_id: Mapped[Optional[int]] = mapped_column(ForeignKey("unified_events.id"),
+    unified_event_id: Mapped[Optional[int]] = mapped_column(ForeignKey("unified_events.id"), comment="深度文章也可归入大事件分析")
-                                                            comment="深度文章也可归入大事件分析")
 
     external_id: Mapped[str] = mapped_column(String(32), comment="RSS原文<guid>生成的MD5防重指纹")
     title_embedding: Mapped[Optional[str]] = mapped_column(Text, comment="新闻标题/摘要的语义向量")
@@ -196,10 +178,6 @@ class NewsArticle(Base):
     created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), default=utcnow)
     updated_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), default=utcnow, onupdate=utcnow)
 
-
-# ==========================================
-# 模块四：热度与轨迹追踪
-# ==========================================
 class HeadlineRevision(Base):
     """
     标题修订历史表
@@ -214,8 +192,7 @@ class HeadlineRevision(Base):
     previous_headline: Mapped[str] = mapped_column(String(255), comment="修改前的旧标题")
     revised_headline: Mapped[str] = mapped_column(String(255), comment="修改后的新标题")
 
-    created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), default=utcnow,
+    created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), default=utcnow, comment="系统发现被修改的时间")
-                                                 comment="系统发现被修改的时间")
 
 
 class RankingLog(Base):
@@ -235,15 +212,10 @@ class RankingLog(Base):
     # 当时它在第几名
     ranking_position: Mapped[int] = mapped_column(Integer, comment="当时抓取时的排名名次")
     # 爬虫看到它的那一瞬间的时间
-    observed_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), default=utcnow,
+    observed_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), default=utcnow, comment="观察到该名次的准确时间")
-                                                  comment="观察到该名次的准确时间")
 
     created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), default=utcnow)
 
-
-# ==========================================
-# 模块五：多态话题与多态评论
-# ==========================================
 class ExtractedTopic(Base):
     """
     AI 提取的核心话题标签表
@@ -290,10 +262,6 @@ class DiscussionComment(Base):
 
     created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), default=utcnow)
 
-
-# ==========================================
-# 模块六：用户画像与多渠道高可用推送系统
-# ==========================================
 class AppUser(Base):
     """
     系统核心用户表
@@ -304,18 +272,10 @@ class AppUser(Base):
     id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
     email: Mapped[str] = mapped_column(String(150), unique=True, index=True, comment="主账号邮箱")
     password_hash: Mapped[Optional[str]] = mapped_column(String(255), comment="密码哈希(第三方登录可为空)")
-
     nickname: Mapped[Optional[str]] = mapped_column(String(100), comment="用户展示昵称")
     avatar_url: Mapped[Optional[str]] = mapped_column(String(500), comment="用户头像地址")
-    gender: Mapped[GenderType] = mapped_column(Enum(GenderType), default=GenderType.UNKNOWN,
+    gender: Mapped[GenderType] = mapped_column(Enum(GenderType), default=GenderType.UNKNOWN, comment="用户性别(用于AI调整行文语气)")
-                                               comment="用户性别(用于AI调整行文语气)")
+    metadata_: Mapped[Optional[Any]] = mapped_column("metadata", JSON, comment="JSON扩展字段: 存放灵活多变的前端用户偏好设置")
-
-    # 极其强大：一个万能收纳箱！前端未来想加任何诸如“夜间模式”、“字体变大”的开关，
-    # 全部丢进这个 JSON 字段即可，从此免去手动修改后端表结构的麻烦。
-    metadata_: Mapped[Optional[Any]] = mapped_column("metadata", JSON,
-                                                     comment="JSON扩展字段: 存放灵活多变的前端用户偏好设置")
-
-    # 时区对于定时推送系统极其重要！保证纽约的用户和北京的用户都能在早晨8点收到新闻。
     timezone: Mapped[str] = mapped_column(String(50), default="Asia/Shanghai", comment="用户所在地时区")
     created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), default=utcnow)
     updated_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), default=utcnow, onupdate=utcnow)
@@ -334,14 +294,10 @@ class UserPushEndpoint(Base):
 
     id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
     user_id: Mapped[int] = mapped_column(ForeignKey("app_users.id"), comment="所属用户ID")
-    # 填入大写的纯字符串，如 EMAIL, WECHAT_BOT, TELEGRAM
     channel_type: Mapped[str] = mapped_column(String(50), comment="推送渠道类型标识")
-    # 具体的发送目标地址
     channel_account: Mapped[str] = mapped_column(String(255), comment="具体的接收账号(邮箱号/微信号/Webhook)")
     is_active: Mapped[bool] = mapped_column(Boolean, default=True, comment="用户是否临时关闭了该渠道")
-    # 高可用容灾：比如 1 代表必须先发微信，如果报错了，再去找 priority=2 的邮箱补发
     priority_level: Mapped[int] = mapped_column(Integer, default=1, comment="推送优先级(1最高，用于错误降级重试)")
-
     created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), default=utcnow)
     updated_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), default=utcnow, onupdate=utcnow)
 
@@ -353,7 +309,6 @@ class UserTopicPreference(Base):
     """
     __tablename__ = "user_topic_preferences"
     __table_args__ = (
-        # 联合防抖限制：防止用户在界面卡顿时连点两次，订阅了两个同样的词
         UniqueConstraint("user_id", "interested_keyword", name="idx_unique_preference"),
     )
 
@@ -390,7 +345,6 @@ class DeliveryHistory(Base):
     """
     __tablename__ = "delivery_history"
     __table_args__ = (
-        # 终极去重约束：一个用户，针对同一篇新闻，永远只允许存在一条记录
         UniqueConstraint("user_id", "target_type", "target_id", name="idx_prevent_duplicate_push"),
     )
 
@@ -398,16 +352,10 @@ class DeliveryHistory(Base):
     user_id: Mapped[int] = mapped_column(ForeignKey("app_users.id"), comment="接收推送的用户")
     target_type: Mapped[TargetType] = mapped_column(Enum(TargetType), comment="推送出去的具体内容类型")
     target_id: Mapped[int] = mapped_column(BigIntType, comment="推送内容的主键ID")
-    # 记录这次推送是彻底成功了，还是由于渠道网络问题失败了
     status: Mapped[TaskStatus] = mapped_column(Enum(TaskStatus), comment="最终推送结果状态")
 
-    created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), default=utcnow,
+    created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), default=utcnow, comment="记录或实际推送的准确时间")
-                                                 comment="记录或实际推送的准确时间")
 
-
-# ==========================================
-# 模块七：系统任务监控
-# ==========================================
 class DataSyncTask(Base):
     """
     数据同步健康度监控表 (运维巡检专用)
@@ -420,7 +368,6 @@ class DataSyncTask(Base):
     source_id: Mapped[int] = mapped_column(ForeignKey("info_sources.id"), comment="本次运行爬取的哪个源")
     items_fetched: Mapped[int] = mapped_column(Integer, default=0, comment="本次爬虫成功插入或更新的新闻条数")
     task_status: Mapped[TaskStatus] = mapped_column(Enum(TaskStatus), comment="该平台的宏观抓取状态")
-    # 如果代码意外崩溃、或是遭遇403/502，把 Python的 traceback 堆栈原封不动存进这里
     error_trace: Mapped[Optional[str]] = mapped_column(Text, comment="若失败则保存完整报错堆栈")
 
     created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), default=utcnow, comment="任务执行的发生时间")

backend/app/prompts/digest_email_template.py

@@ -1,7 +1,3 @@
-# 推送邮件 HTML 模板
-# 用于生成定时推送给用户的热点摘要邮件
-
-# 邮件客户端不支持 Font Awesome，改用 Emoji 代替平台图标
 PLATFORM_EMOJI: dict[str, str] = {
     "微博热搜": "🔴",
     "微博": "🔴",
@@ -86,7 +82,7 @@ body{{margin:0;padding:0;background:#0d1117;color:#e6edf3;font-family:-apple-sys
 <body>
 <div class="container">
   <div class="header">
-    <h1>InsightRadar · 热点快报</h1>
+    <h1>聚势智见 · 热点快报</h1>
     <p>{delivery_time} · 为你精选了 {event_count} 条事件</p>
     <span class="mode-badge {mode_badge_class}">{mode_label}</span>
   </div>
@@ -94,8 +90,8 @@ body{{margin:0;padding:0;background:#0d1117;color:#e6edf3;font-family:-apple-sys
   {event_cards_html}
 
   <div class="footer">
-    <p>此邮件由 InsightRadar 自动推送。</p>
+    <p>此邮件由 聚势智见自动推送。</p>
-    <p>如需调整推送设置，请登录 <a href="{app_url}">InsightRadar 控制台</a></p>
+    <p>如需调整推送设置，请登录 <a href="{app_url}">聚势智见 控制台</a></p>
   </div>
 </div>
 </body>

backend/app/schemas/delivery_schema.py

@@ -1,9 +1,9 @@
-# 推送设置相关的请求/响应模型
 from datetime import datetime
 from typing import List, Optional
 
 from pydantic import BaseModel, ConfigDict, Field
 
+# AI辅助生成：deepseek-v3-2，2026年3月20日
 
 # ==========================================
 # 推送时间表 (UserDeliverySchedule)

backend/app/schemas/event_schema.py

@@ -1,9 +1,7 @@
-# app/schemas/event_schema.py
 from pydantic import BaseModel, Field
 from typing import List, Optional
 from datetime import datetime
 
-
 class PlatformTrendResponse(BaseModel):
     source_id: int
     platform_name: str

backend/app/schemas/preference_schema.py

@@ -3,7 +3,6 @@ from typing import List, Optional
 
 from pydantic import BaseModel, ConfigDict, Field
 
-
 class UserTopicPreferenceCreate(BaseModel):
     """新增用户兴趣词请求体。"""
     interested_keyword: str = Field(..., min_length=1, max_length=100, description="用户感兴趣的关键词")

backend/app/schemas/source_schema.py

@@ -1,4 +1,3 @@
-# app/schemas/source_schema.py
 from pydantic import BaseModel, ConfigDict, Field
 from typing import Optional
 from datetime import datetime
@@ -6,6 +5,7 @@ from datetime import datetime
 # 枚举
 from app.models.models import SourceType
 
+# AI辅助生成：deepseek-v3-2，2026年3月20日
 
 # ==========================================
 # InfoSource (信息源) 相关的 Schemas

backend/app/services/delivery_service.py

@@ -1,7 +1,3 @@
-# 定时推送调度服务
-# 由 APScheduler 每分钟调用，检查当前时刻是否有用户需要接收推送，
-# 如匹配则生成摘要邮件并发送，同时写入 DeliveryHistory 防重复。
-# 推送优先级：有关键词且匹配 → 个性化简报；无关键词或无匹配 → 默认热点快报
 import logging
 import os
 from logging.handlers import TimedRotatingFileHandler
@@ -34,7 +30,7 @@ from app.utils.email_utils import send_html_email
 
 logger = logging.getLogger("delivery_service")
 
-# delivery_service 日志单独写文件
+
 _delivery_log_dir = Path(__file__).resolve().parents[2] / "logs"
 _delivery_log_dir.mkdir(parents=True, exist_ok=True)
 _delivery_log_file = _delivery_log_dir / "delivery_check.log"
@@ -51,6 +47,8 @@ if not logger.handlers:
 logger.setLevel(logging.INFO)
 logger.propagate = False
 
+# AI辅助生成：deepseek-v3-2，2026年3月20日
+
 # 推送时间窗口：实际执行时刻与设定时间的最大容差（分钟）
 DELIVERY_WINDOW_MINUTES = int(os.getenv("DELIVERY_WINDOW_MINUTES", 2))
 # 同一用户两次推送之间的最小间隔（分钟）
@@ -64,13 +62,10 @@ DEFAULT_MODE_HOURS = int(os.getenv("DEFAULT_MODE_HOURS", 24))
 # 用户时区无效时的兜底时区
 DEFAULT_FALLBACK_TIMEZONE = os.getenv("DEFAULT_FALLBACK_TIMEZONE", "Asia/Shanghai")
 
-
-# ==========================================
-# 默认热点事件容器（无关键词时使用）
-# ==========================================
 @dataclass
 class _DefaultEventItem:
     """
+    默认热点事件容器
     无关键词订阅或关键词无匹配时的默认热点包装器，
     接口与 MatchedEventResult 保持一致，方便统一传给模板。
     """
@@ -81,10 +76,6 @@ class _DefaultEventItem:
     tags: list[str] = field(default_factory=list)
     is_default: bool = True
 
-
-# ==========================================
-# 时区工具
-# ==========================================
 def _time_to_minutes(t: dt_time) -> int:
     return t.hour * 60 + t.minute
 
@@ -125,10 +116,10 @@ def _ensure_aware(dt: datetime) -> datetime:
         return dt.replace(tzinfo=timezone.utc)
     return dt
 
+# AI辅助生成结束
+
 
-# ==========================================
 # 数据库查询辅助
-# ==========================================
 def _should_skip_by_interval(db: Session, user_id: int) -> bool:
     """检查用户是否仍在冷却期内，避免短时间内重复推送"""
     row = (
@@ -297,9 +288,9 @@ def _record_delivery(
     db.commit()
 
 
-# ==========================================
+# AI辅助生成：deepseek-v3-2，2026年3月20日
+
 # 推送准备
-# ==========================================
 @dataclass
 class _PendingPush:
     """暂存需要发送邮件的信息，便于在 async 上下文中发送。"""
@@ -309,6 +300,7 @@ class _PendingPush:
     html_body: str
     event_ids: list[int]
 
+# AI生成结束
 
 def _prepare_user_push(db: Session, user: AppUser, schedule: UserDeliverySchedule) -> _PendingPush | None:
     """
@@ -331,7 +323,6 @@ def _prepare_user_push(db: Session, user: AppUser, schedule: UserDeliverySchedul
 
     pushed_ids = _get_already_pushed_event_ids(db, user_id)
 
-    # 决策：有关键词且有匹配 → 匹配模式；否则 → 默认热点模式
     items: list = []
     is_default = False
 
@@ -361,7 +352,6 @@ def _prepare_user_push(db: Session, user: AppUser, schedule: UserDeliverySchedul
             logger.info(f"用户 {user_id} 默认热点无可推送内容，跳过")
             return None
 
-    # 批量加载平台数据（来源名、标题、URL、排名）
     event_ids = [item.event.id for item in items]
     platforms_map = _load_event_platforms(db, event_ids)
 
@@ -377,15 +367,12 @@ def _prepare_user_push(db: Session, user: AppUser, schedule: UserDeliverySchedul
     return _PendingPush(
         user_id=user_id,
         email_targets=[ep.channel_account for ep in email_endpoints],
-        subject=f"InsightRadar {subject_suffix} · {time_str}",
+        subject=f"聚势智见 {subject_suffix} · {time_str}",
         html_body=html_body,
         event_ids=event_ids,
     )
 
 
-# ==========================================
-# 调度主入口
-# ==========================================
 async def check_and_deliver() -> None:
     """
     定时推送主入口，由 APScheduler 每分钟调用。
@@ -412,7 +399,6 @@ async def check_and_deliver() -> None:
             if not user:
                 continue
 
-            # 将 UTC 转为用户本地时间，判断是否落在推送窗口内
             user_current = _user_local_time(now, user.timezone)
             if not _is_within_window(schedule.delivery_time, user_current):
                 continue
@@ -422,7 +408,6 @@ async def check_and_deliver() -> None:
                 if pending is None:
                     continue
 
-                # 异步按优先级尝试各邮件渠道
                 sent = False
                 for target_email in pending.email_targets:
                     try:

backend/app/services/fetcher_service.py

@@ -1,8 +1,3 @@
-# app/services/fetcher_service.py
-"""
-抓取服务：从外部 API 拉取热搜/RSS 数据，做查重、向量聚类、入库
-热搜分支：语义聚类到 UnifiedEvent；RSS 分支：写入 NewsArticle
-"""
 import os
 import hashlib
 from datetime import timedelta
@@ -19,6 +14,8 @@ from app.models.models import (
     HeadlineRevision, RankingLog, SourceType, utcnow, UnifiedEvent
 )
 
+# AI辅助生成：deepseek-v3-2，2026年3月20日
+
 # 加载环境变量
 load_dotenv()
 hf_token = os.getenv("HF_TOKEN")
@@ -26,11 +23,13 @@ SIMILARITY_THRESHOLD = float(os.getenv("SIMILARITY_THRESHOLD", 0.72))
 API_BASE_URL = os.getenv("API_BASE_URL", "https://newsnow.busiyi.world/api/s")
 EMBEDDING_MODEL_PATH = os.getenv("EMBEDDING_MODEL_PATH", "")
 
-print("正在加载 BAAI/bge-m3 向量模型...")
+print("正在加载模型...")
 # 全局单例
 embedder_model = SentenceTransformer(EMBEDDING_MODEL_PATH, local_files_only=True)
 print("模型加载完成。")
 
+# AI生成结束
+
 
 def generate_md5(text: str) -> str:
     """生成 32 位 MD5 作为 external_id，用于跨平台去重"""
@@ -88,10 +87,10 @@ class UnifiedEventClusterer:
         new_unified = UnifiedEvent(
             unified_title=title,
             center_embedding=embedding_json,
-            hot_score=1  # 初始热度
+            hot_score=1
         )
         self.db.add(new_unified)
-        self.db.flush()  # 获取自增的主键 ID
+        self.db.flush()
         
         # 更新缓存
         self.event_vectors.append(new_vec)
@@ -109,11 +108,8 @@ def process_hot_trend_item(db, source, item, index: int, external_id: str, exist
 
     event_to_log = None
 
-    # 查重：已存在则可能只需更新标题/排名；不存在则需聚类并新建
     if existing_event:
-        # 场景 A1：老熟人
         if existing_event.current_headline != title:
-            # 标题被暗改，此时需要重新算一次 Embedding
             new_embedding_json, _ = embeddings_dict[title]
 
             revision = HeadlineRevision(
@@ -123,30 +119,25 @@ def process_hot_trend_item(db, source, item, index: int, external_id: str, exist
             )
             db.add(revision)
             existing_event.current_headline = title
-            existing_event.title_embedding = new_embedding_json  # 更新为新标题的语义向量
+            existing_event.title_embedding = new_embedding_json
-            # 注：这里不改变它所属的 unified_event_id，因为大体还是同一件事
 
         existing_event.current_ranking = index
         existing_event.event_url = item_url
         event_to_log = existing_event
 
     else:
-        # 场景 A2：这是一条彻底的全新热搜
-        # 1. 计算向量
-        new_embedding_json, new_vec = embeddings_dict[title]
 
-        # 2. 扔进聚类中枢找归宿
+        new_embedding_json, new_vec = embeddings_dict[title]
         matched_event_id = clusterer.match_or_create(title, new_embedding_json, new_vec)
 
-        # 3. 落库
         new_event = TrendingEvent(
             source_id=source.id,
             external_id=external_id,
             current_headline=title,
             event_url=item_url,
             current_ranking=index,
-            title_embedding=new_embedding_json,  # 存入向量
+            title_embedding=new_embedding_json,
-            unified_event_id=matched_event_id  # 挂载到大事件下
+            unified_event_id=matched_event_id
         )
         db.add(new_event)
         db.flush()
@@ -192,7 +183,6 @@ def process_source_data(db, source, items: list) -> int:
     saved_count = 0
     platform_id = source.home_url
 
-    # 1. 批量计算外部 ID 并聚合要计算的文本
     valid_items = []
     external_ids = []
     for item in items:
@@ -209,7 +199,6 @@ def process_source_data(db, source, items: list) -> int:
     if not valid_items:
         return 0
 
-    # 批量查重：按 external_id 判断是更新还是新增
     existing_events_dict = {}
     existing_articles_dict = {}
     
@@ -226,7 +215,6 @@ def process_source_data(db, source, items: list) -> int:
         ).all()
         existing_articles_dict = {art.external_id: art for art in existing_articles}
 
-    # 仅对需要算向量的标题做批量 embedding，避免重复计算
     texts_to_embed = []
     if source.source_type in (SourceType.HOT_TREND, SourceType.API):
         for item, external_id in valid_items:
@@ -238,15 +226,12 @@ def process_source_data(db, source, items: list) -> int:
             else:
                 texts_to_embed.append(title)
                 
-    # 4. 批量执行大模型推理
     embeddings_dict = generate_embeddings_batch(texts_to_embed)
     
-    # 初始化聚类器（只在热搜模式下需要，且只初始化一次）
     clusterer = None
     if source.source_type in (SourceType.HOT_TREND, SourceType.API):
         clusterer = UnifiedEventClusterer(db)
 
-    # 按来源类型分流：热搜/API → TrendingEvent + 聚类；RSS → NewsArticle
     for index, (item, external_id) in enumerate(valid_items, 1):
         if source.source_type in (SourceType.HOT_TREND, SourceType.API):
             existing_event = existing_events_dict.get(external_id)
@@ -269,14 +254,12 @@ async def fetch_and_save_trending_data():
     """
     print(f"[{utcnow()}] 开始执行定时抓取任务...")
 
-    # 获取启用的信息源 - 这个只读操作用一个短连接
     with SessionLocal() as db:
         sources = db.query(InfoSource).filter(InfoSource.is_enabled == True).all()
         if not sources:
             print("没有找到启用的信息源，任务结束。")
             return
             
-        # 我们把 source 的信息提前提取出来，避免在异步中长期持有 session
         source_configs = [
             {
                 "id": s.id,
@@ -287,7 +270,6 @@ async def fetch_and_save_trending_data():
             for s in sources
         ]
 
-    # 伪装请求头，规避反爬
     custom_headers = {
         "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/145.0.0.0 Safari/537.36",
         "Accept": "application/json, text/plain, */*",
@@ -304,13 +286,11 @@ async def fetch_and_save_trending_data():
             url = f"{API_BASE_URL}?id={platform_id}&latest"
 
             try:
-                # 1. 网络请求（可能耗时较长，不要包在 db session 里）
                 response = await client.get(url)
                 response.raise_for_status()
                 data_json = response.json()
                 items = data_json.get("items", [])
                 
-                # 2. 数据库事务操作（尽量短，单独使用 session）
                 with SessionLocal() as db:
                     # 重新从短 session 中获取 source 实例，以免 detached
                     source = db.query(InfoSource).get(s_config["id"])
@@ -319,10 +299,8 @@ async def fetch_and_save_trending_data():
                         
                     task_log = DataSyncTask(source_id=source.id, items_fetched=0)
                     try:
-                        # 调用数据处理层
                         saved_count = process_source_data(db, source, items)
 
-                        # 业务事务成功提交
                         task_log.items_fetched = saved_count
                         task_log.task_status = TaskStatus.SUCCESS
                         db.add(task_log)
@@ -330,10 +308,9 @@ async def fetch_and_save_trending_data():
                         print(f"[{source.source_name}] ({source.source_type}) 成功抓取并更新了 {saved_count} 条数据")
                     except Exception as e:
                         db.rollback()
-                        raise e # 抛出给外层捕获记录日志
+                        raise e
                         
             except Exception as e:
-                # 异常拦截与错误隔离，另起一个超短事务记录日志
                 with SessionLocal() as log_db:
                     try:
                         new_task_log = DataSyncTask(source_id=s_config["id"], items_fetched=0)

backend/app/services/matching_service.py

@@ -1,7 +1,3 @@
-"""
-匹配服务：根据用户兴趣关键词（精确 + 语义）推荐事件
-打分融合：匹配分 + 标签相关度 + 热度 + 新鲜度加成
-"""
 import os
 from dataclasses import dataclass
 from datetime import datetime, timedelta, timezone
@@ -13,8 +9,9 @@ from sqlalchemy.orm import Session
 from app.models.models import ExtractedTopic, TargetType, UnifiedEvent, UserTopicPreference, utcnow
 from app.services.fetcher_service import embedder_model
 
+# AI辅助生成：deepseek-v3-2，2026年3月20日
 
-# 语义匹配阈值：用户关键词和事件标签向量相似度达到该值才计入语义命中
+# 语义匹配阈值：用户关键词和事件标签/标题向量相似度达到该值才计入语义命中
 DEFAULT_PREFERENCE_SEMANTIC_THRESHOLD = 0.78
 PREFERENCE_SEMANTIC_THRESHOLD = float(
     os.getenv("PREFERENCE_SEMANTIC_THRESHOLD", str(DEFAULT_PREFERENCE_SEMANTIC_THRESHOLD))
@@ -35,12 +32,38 @@ class MatchedEventResult:
     semantic_hits: list[dict[str, Any]]
     tags: list[str]
 
+# AI生成结束
 
 def _normalize_text(text: str) -> str:
     """统一小写与首尾空白，便于做稳定匹配。"""
     return text.strip().casefold()
 
 
+def _find_exact_preference_match(
+    target_text: str,
+    normalized_preferences: list[tuple[str, str]],
+) -> str | None:
+    """
+    判断目标文本是否与某个用户兴趣词形成“精确命中”。
+    命中条件：
+    1. 标准化后完全相等
+    2. 二者互为包含关系
+    返回命中的原始兴趣词，未命中则返回 None。
+    """
+    normalized_target = _normalize_text(target_text)
+    if not normalized_target:
+        return None
+
+    for raw_pref, normalized_pref in normalized_preferences:
+        if not normalized_pref:
+            continue
+        if normalized_target == normalized_pref:
+            return raw_pref
+        if normalized_pref in normalized_target or normalized_target in normalized_pref:
+            return raw_pref
+    return None
+
+
 _EMBEDDING_CACHE: dict[str, np.ndarray] = {}
 MAX_CACHE_SIZE = 10000
 
@@ -55,7 +78,6 @@ def _build_keyword_embedding_map(keywords: list[str]) -> dict[str, np.ndarray]:
         
     uncached_keywords = []
     
-    # 1. 尝试从缓存获取
     for keyword in keywords:
         if not keyword:
             continue
@@ -64,9 +86,7 @@ def _build_keyword_embedding_map(keywords: list[str]) -> dict[str, np.ndarray]:
         else:
             uncached_keywords.append(keyword)
             
-    # 2. 对未命中的词进行统一的批量推理
     if uncached_keywords:
-        # 去重，避免同一个未缓存的词被计算多次
         unique_uncached = list(dict.fromkeys(uncached_keywords))
         
         vectors = embedder_model.encode(unique_uncached, normalize_embeddings=True, show_progress_bar=False)
@@ -77,7 +97,6 @@ def _build_keyword_embedding_map(keywords: list[str]) -> dict[str, np.ndarray]:
             for k in keys_to_delete:
                 del _EMBEDDING_CACHE[k]
                 
-        # 3. 将新计算的向量存入缓存并回填结果
         for keyword, vec in zip(unique_uncached, vectors):
             vec_array = np.asarray(vec, dtype=np.float32)
             _EMBEDDING_CACHE[keyword] = vec_array
@@ -86,6 +105,26 @@ def _build_keyword_embedding_map(keywords: list[str]) -> dict[str, np.ndarray]:
     return result
 
 
+def _find_best_semantic_match(
+    target_text: str,
+    target_vec_map: dict[str, np.ndarray],
+    pref_vec_map: dict[str, np.ndarray],
+) -> tuple[str | None, float]:
+    """返回与目标文本最接近的兴趣词及其余弦相似度。"""
+    target_vec = target_vec_map.get(target_text)
+    if target_vec is None:
+        return None, -1.0
+
+    best_pref = None
+    best_sim = -1.0
+    for pref_keyword, pref_vec in pref_vec_map.items():
+        sim = float(np.dot(target_vec, pref_vec))
+        if sim > best_sim:
+            best_sim = sim
+            best_pref = pref_keyword
+    return best_pref, best_sim
+
+
 def _ensure_aware(dt: datetime) -> datetime:
     """SQLite 读出的 datetime 不带时区信息，统一补上 UTC 后才能和 utcnow() 做减法。"""
     if dt.tzinfo is None:
@@ -116,8 +155,8 @@ def recommend_events_for_user(
 ) -> list[MatchedEventResult]:
     """
     用户兴趣推荐主流程：
-    1) 精确匹配：用户词 == EVENT 标签
+    1) 精确匹配：用户词 vs EVENT 标签/标题
-    2) 语义匹配：用户词向量 vs EVENT 标签向量（超过阈值）
+    2) 语义匹配：用户词向量 vs EVENT 标签/标题向量（超过阈值）
     3) 打分融合：匹配分 + 标签相关度 + 热度 + 新鲜度
     """
     final_limit = max(1, min(limit, PREFERENCE_RECOMMEND_MAX_LIMIT))
@@ -127,7 +166,6 @@ def recommend_events_for_user(
         else PREFERENCE_SEMANTIC_THRESHOLD
     )
 
-    # 1. 读取用户兴趣词
     preferences = (
         db.query(UserTopicPreference)
         .filter(UserTopicPreference.user_id == user_id)
@@ -140,7 +178,6 @@ def recommend_events_for_user(
     if not preference_keywords:
         return []
 
-    # 2. 读取候选事件（时间 + 热度过滤，避免全表扫描）
     time_limit = utcnow() - timedelta(hours=hours)
     events = (
         db.query(UnifiedEvent)
@@ -167,72 +204,47 @@ def recommend_events_for_user(
         )
         .all()
     )
-    if not topic_rows:
-        return []
 
-    # 组织事件标签映射：event_id -> [(tag, relevance_score), ...]
     event_topics: dict[int, list[tuple[str, float | None]]] = {}
     for event_id, topic_keyword, relevance_score in topic_rows:
         if not topic_keyword:
             continue
         event_topics.setdefault(event_id, []).append((topic_keyword, relevance_score))
 
-    # 如果某事件没有标签，就不参与推荐
-    if not event_topics:
-        return []
-
-    # 3. 批量编码用户词与标签词，减少模型调用次数
     unique_preference_keywords = list(dict.fromkeys(preference_keywords))
     unique_topic_keywords = list(dict.fromkeys([row[1] for row in topic_rows if row[1]]))
     pref_vec_map = _build_keyword_embedding_map(unique_preference_keywords)
     topic_vec_map = _build_keyword_embedding_map(unique_topic_keywords)
 
-    # 预先建立“标准化后用户词集合”，用于精确匹配
+    normalized_preference_pairs = [
-    normalized_pref_set = {_normalize_text(word) for word in unique_preference_keywords}
+        (word, _normalize_text(word))
+        for word in unique_preference_keywords
+        if _normalize_text(word)
+    ]
+    unique_event_titles = list(
+        dict.fromkeys(
+            [event.unified_title.strip() for event in events if event.unified_title and event.unified_title.strip()]
+        )
+    )
+    title_vec_map = _build_keyword_embedding_map(unique_event_titles)
 
     scored_results: list[MatchedEventResult] = []
     for event in events:
         topic_list = event_topics.get(event.id, [])
-        if not topic_list:
-            continue
 
         exact_hits: list[str] = []
         semantic_hits: list[dict[str, Any]] = []
         score = 0.0
-
-        # 对每个事件标签做精确匹配或语义匹配
         for topic_keyword, topic_relevance in topic_list:
-            normalized_topic = _normalize_text(topic_keyword)
             topic_relevance_score = float(topic_relevance) if topic_relevance is not None else 50.0
             
-            # 1) 精确命中（包括完全相等与包含关系）
+            matched_pref = _find_exact_preference_match(topic_keyword, normalized_preference_pairs)
-            matched_exact = False
+            if matched_pref is not None:
-            if normalized_topic in normalized_pref_set:
-                matched_exact = True
-            else:
-                for pref_word in normalized_pref_set:
-                    if pref_word and (pref_word in normalized_topic or normalized_topic in pref_word):
-                        matched_exact = True
-                        break
-
-            if matched_exact:
                 exact_hits.append(topic_keyword)
-                # 精确命中给较高基础分，标签自身相关度作为增益
                 score += 45.0 + topic_relevance_score * 0.2
                 continue
 
-            # 2) 语义命中（未精确命中时再算）
+            best_pref, best_sim = _find_best_semantic_match(topic_keyword, topic_vec_map, pref_vec_map)
-            topic_vec = topic_vec_map.get(topic_keyword)
-            if topic_vec is None:
-                continue
-
-            best_pref = None
-            best_sim = -1.0
-            for pref_keyword, pref_vec in pref_vec_map.items():
-                sim = float(np.dot(topic_vec, pref_vec))
-                if sim > best_sim:
-                    best_sim = sim
-                    best_pref = pref_keyword
 
             if best_pref is not None and best_sim >= similarity_threshold:
                 semantic_hits.append(
@@ -242,18 +254,32 @@ def recommend_events_for_user(
                         "similarity": round(best_sim, 4),
                     }
                 )
-                # 语义命中分略低于精确命中，并由相似度放大
                 score += best_sim * 35.0 + topic_relevance_score * 0.12
 
-        # 如果精确和语义都没命中，直接跳过
+        event_title = (event.unified_title or "").strip()
+        if event_title:
+            title_exact_pref = _find_exact_preference_match(event_title, normalized_preference_pairs)
+            if title_exact_pref is not None:
+                exact_hits.append(f"标题:{title_exact_pref}")
+                score += 30.0
+            else:
+                best_pref, best_sim = _find_best_semantic_match(event_title, title_vec_map, pref_vec_map)
+                if best_pref is not None and best_sim >= similarity_threshold:
+                    semantic_hits.append(
+                        {
+                            "preference_keyword": best_pref,
+                            "topic_keyword": f"标题:{best_pref}",
+                            "similarity": round(best_sim, 4),
+                        }
+                    )
+                    score += best_sim * 24.0
+
         if not exact_hits and not semantic_hits:
             continue
 
-        # 融合事件热度和新鲜度，避免只看语义分
         score += min(event.hot_score, 100) * 0.3
         score += _calc_freshness_bonus(event)
 
-        # 返回标签时做去重，保证接口稳定
         tags = list(dict.fromkeys([item[0] for item in topic_list]))
         scored_results.append(
             MatchedEventResult(

backend/app/services/summary_service.py

@@ -1,8 +1,3 @@
-# app/services/summary_service.py
-"""
-摘要服务：调用 LLM 生成统一标题、综合摘要、话题标签
-定时任务：对热度达标且未摘要的事件批量处理
-"""
 import json
 import os
 from datetime import timedelta
@@ -26,12 +21,16 @@ from app.prompts.summary_prompts import (
 )
 from app.services.fetcher_service import embedder_model
 
+# AI辅助生成：deepseek-v3-2，2026年3月20日
+
 HOT_SCORE_THRESHOLD = int(os.getenv("HOT_SCORE_THRESHOLD", 3))
 TOPIC_TAG_MIN_HOT_SCORE = int(os.getenv("TOPIC_TAG_MIN_HOT_SCORE", HOT_SCORE_THRESHOLD))
 TOPIC_SIMILARITY_THRESHOLD = float(os.getenv("TOPIC_SIMILARITY_THRESHOLD", 0.82))
 TOPIC_TAG_MAX_COUNT = int(os.getenv("TOPIC_TAG_MAX_COUNT", 8))
 AI_API_KEY = os.getenv("AI_API_KEY", "")
 
+# AI生成结束
+
 
 deepseek_client = AsyncOpenAI(
     api_key=AI_API_KEY,
@@ -184,7 +183,6 @@ async def generate_unified_summaries():
     """定时任务：对热度达标且未摘要的事件刷新标题、摘要、标签"""
     print(f"[{utcnow()}] Start unified summary generation task...")
 
-    # 先提取需要处理的事件 ID，尽早释放 session，不长期占用 db session
     with SessionLocal() as db:
         recent_threshold = utcnow() - timedelta(days=3)
         events = db.query(UnifiedEvent).filter(
@@ -197,11 +195,9 @@ async def generate_unified_summaries():
             print("No events require summary update in this round.")
             return
             
-        # 复制出需要的信息，脱离 session
         event_ids = [e.id for e in events]
         event_hot_scores = {e.id: e.hot_score for e in events}
 
-    # 外层循环：针对每个 event_id 开启一个极短生命周期的 session 获取依赖数据
     for event_id in event_ids:
         platform_dict: dict[str, set[str]] = {}
         with SessionLocal() as db:

backend/app/utils/email_utils.py

@@ -1,4 +1,4 @@
-# app/utils/email_utils.py
+# AI辅助生成：deepseek-v3-2，2026年3月20日
 import os
 from email.message import EmailMessage
 import aiosmtplib

backend/dockerfile

@@ -1,31 +0,0 @@
-FROM python:3.11-slim AS builder
-
-WORKDIR /insightradar
-
-COPY pyproject.toml uv.lock ./
-
-RUN --mount=type=cache,target=/root/.cache/uv \
-    pip install --no-cache-dir uv && \
-    uv sync --frozen --no-dev
-
-COPY app app
-COPY main.py main.py
-
-#-----------------------------------------------
-
-FROM python:3.11-slim
-
-WORKDIR /insightradar
-
-# 👇 复制虚拟环境
-COPY --from=builder /insightradar/.venv /insightradar/.venv
-
-COPY app app
-COPY main.py main.py
-
-# 👇 关键：用 venv 里的 python
-ENV PATH="/insightradar/.venv/bin:$PATH"
-
-EXPOSE 8000
-
-CMD ["python","main.py"]

backend/main.py

@@ -1,12 +1,16 @@
-# run.py
+# AI辅助生成：deepseek-v3-2，2026年3月20日
 import uvicorn
+import os
+from dotenv import load_dotenv
 
 if __name__ == "__main__":
-    # 启动服务
+    
+    load_dotenv()
+    PORT = int(os.getenv("PORT", 8000))
+    
     uvicorn.run(
         app="app.main:app",
         host="0.0.0.0",
-        port=8000,
+        port=PORT,
-        # reload=True,
         workers=1
     )

backend/pyproject.toml

@@ -49,7 +49,6 @@ dependencies = [
     "safetensors==0.7.0",
     "scikit-learn==1.8.0",
     "scipy==1.17.1",
-    "sentence-transformers==5.2.3",
     "shellingham==1.5.4",
     "sniffio==1.3.1",
     "sqlalchemy==2.0.48",
@@ -57,8 +56,6 @@ dependencies = [
     "sympy==1.14.0",
     "threadpoolctl==3.6.0",
     "tokenizers==0.22.2",
-    "torch==2.10.0",
-    "torchvision==0.25.0",
     "tqdm==4.67.3",
     "transformers==5.3.0",
     "typer==0.24.1",
@@ -68,4 +65,16 @@ dependencies = [
     "tzlocal==5.3.1",
     "urllib3==2.6.3",
     "uvicorn==0.41.0",
+    "torch==2.11.0+cpu",
+    "torchvision==0.26.0+cpu",
+    "torchaudio==2.11.0+cpu",
+    "sentence-transformers>=5.3.0",
 ]
+
+[[tool.uv.index]]
+name = "pytorch-cpu"
+url = "https://download.pytorch.org/whl/cpu"
+default = false
+
+[tool.uv]
+index-strategy = "unsafe-best-match"

dockerfile

@@ -0,0 +1,50 @@
+# ---------- 阶段1：前端编译（Node打包静态产物） ----------
+FROM node:22-alpine AS frontend-builder
+
+WORKDIR /frontend
+
+# 复制前端依赖，利用Docker缓存优化
+COPY frontend/package*.json ./
+RUN npm install --registry=https://registry.npmmirror.com
+
+# 复制前端代码，编译出静态产物
+COPY frontend/ .
+RUN npm run build
+
+# ---------- 阶段2：后端依赖构建（uv构建虚拟环境） ----------
+FROM python:3.11-slim AS backend-builder
+
+WORKDIR /backend
+
+# 安装uv，同步Python依赖
+COPY backend/pyproject.toml backend/uv.lock ./
+RUN --mount=type=cache,target=/root/.cache/uv \
+    pip install --no-cache-dir uv && \
+    uv sync --frozen --no-dev --index https://pypi.tuna.tsinghua.edu.cn/simple/
+
+# 复制后端代码
+COPY backend/app ./app
+COPY backend/main.py ./
+
+# ---------- 阶段3：最终运行镜像（仅Python+Uvicorn，托管前端静态） ----------
+FROM python:3.11-slim
+
+WORKDIR /app
+
+# 复制构建好的后端虚拟环境
+COPY --from=backend-builder /backend/.venv /app/.venv
+COPY --from=backend-builder /backend/app /app/app
+COPY --from=backend-builder /backend/main.py /app/main.py
+
+# 复制前端编译好的静态产物，放到后端能访问的目录
+# 这里我们把静态文件放到 /app/static 目录
+COPY --from=frontend-builder /frontend/dist /app/app/static
+
+# 把venv加入PATH
+ENV PATH="/app/.venv/bin:$PATH"
+
+# 暴露Uvicorn端口
+EXPOSE 8000
+
+# 直接启动Uvicorn，由Uvicorn配合后端框架托管静态文件
+CMD ["python3", "main.py"]

frontend/index.html

@@ -4,8 +4,7 @@
     <meta charset="UTF-8">
     <link rel="icon" href="/favicon.svg">
     <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>InsightRadar - 全网热点监控中枢</title>
+    <title>聚势智见 - 基于语义聚类与大模型的热点资讯聚合平台</title>
-    <!-- Font Awesome 图标库 -->
     <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.5.1/css/all.min.css">
   </head>
   <body>

frontend/src/api/delivery.ts

@@ -6,9 +6,6 @@ export function fetchDeliveryConfig(userId: number): Promise<DeliveryConfig> {
   return apiGet<DeliveryConfig>(`/users/${userId}/delivery-config`)
 }
 
-// ==========================================
-// 推送时间表
-// ==========================================
 export function createDeliverySchedule(
   userId: number,
   payload: { delivery_time: string; is_active?: boolean },
@@ -34,9 +31,6 @@ export function deleteDeliverySchedule(
   return apiDelete(`/users/${userId}/delivery-schedules/${scheduleId}`)
 }
 
-// ==========================================
-// 推送渠道
-// ==========================================
 export function createPushEndpoint(
   userId: number,
   payload: {

frontend/src/assets/main.css

@@ -1,4 +1,4 @@
-@import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&family=Noto+Sans+SC:wght@400;500;600;700&display=swap');
+@import url(./font.css);
 
 /* =========================================
    1. 现代 SaaS 风格高级主题变量

frontend/src/auth.api.ts

@@ -1,6 +1,3 @@
-/**
- * 认证 API：登录、注册、发送验证码（不走通用 client，无 Bearer）
- */
 import type {
   AuthTokenResponse,
   LoginPayload,

frontend/src/auth.types.ts

@@ -1,3 +1,5 @@
+// AI辅助生成：deepseek-v3-2，2026年3月20日
+
 export interface UserProfile {
   id: number
   email: string

frontend/src/components/UnifiedEventCard.vue

@@ -111,6 +111,14 @@ function getRankingChartOptions(history: number[], platformColor: string) {
       height: 56,
       sparkline: { enabled: true },
       animations: { enabled: true, easing: 'easeinout' as const, speed: 400 },
+      events: {
+        mounted: (chartContext: any) => {
+          chartContext.el?.querySelector('.apexcharts-svg > title')?.remove()
+        },
+        updated: (chartContext: any) => {
+          chartContext.el?.querySelector('.apexcharts-svg > title')?.remove()
+        }
+      }
     },
     stroke: { curve: 'smooth' as const, width: 2 },
     fill: {

frontend/src/layouts/DashboardLayout.vue

@@ -1,3 +1,4 @@
+<!-- AI辅助生成：deepseek-v3-2，2026年3月20日 -->
 <!-- 仪表盘布局：侧边栏导航、主内容区、移动端抽屉 -->
 <script setup lang="ts">
 import { computed, ref } from 'vue'
@@ -57,7 +58,7 @@ function toggleSidebar() {
         <!-- Logo -->
         <div class="sidebar-logo">
           <BrandLogo />
-          <span class="logo-text">InsightRadar<span class="logo-dot">.AI</span></span>
+          <span class="logo-text">聚势智见<span class="logo-dot">.AI</span></span>
         </div>
 
         <!-- 导航菜单 -->

frontend/src/main.ts

@@ -1,6 +1,4 @@
-/**
+// AI辅助生成：deepseek-v3-2，2026年3月20日
- * 应用入口：初始化 Vue、Pinia、路由、主题
- */
 import './assets/main.css'
 
 import { createApp } from 'vue'

frontend/src/views/AboutView.vue

@@ -1,7 +1,9 @@
+<!-- AI辅助生成：deepseek-v3-2，2026年3月20日 -->
+
 <!-- 关于页（占位） -->
 <template>
   <div class="about">
-    <h1>关于 InsightRadar</h1>
+    <h1>关于 聚势智见</h1>
   </div>
 </template>
 

frontend/src/views/DashboardView.vue

@@ -1,4 +1,3 @@
-<!-- 主仪表盘：事件流、为你推荐、公关修改追踪、系统状态 -->
 <script setup lang="ts">
 import { onMounted, ref, computed, watch } from 'vue'
 import { useRoute, useRouter } from 'vue-router'
@@ -13,9 +12,6 @@ import type { MatchedEvent, UserTopicPreference } from '@/types/preference'
 const route = useRoute()
 const router = useRouter()
 
-// ==========================================
-// 聚光灯：从推荐页跳转过来时，按 ID 单独拉取目标事件
-// ==========================================
 const spotlightEvent = ref<UnifiedEvent | null>(null)
 const loadingSpotlight = ref(false)
 
@@ -41,9 +37,7 @@ function dismissSpotlight() {
 const authStore = useAuthStore()
 const userId = computed(() => authStore.user?.id ?? 0)
 
-// ==========================================
+
-// 状态
-// ==========================================
 const events = ref<UnifiedEvent[]>([])
 const revisions = ref<HeadlineRevision[]>([])
 const stats = ref<SystemStats | null>(null)
@@ -101,9 +95,6 @@ const recSortOptions = [
   { label: '最新', value: 'created_at' },
 ]
 
-// ==========================================
-// 平台视觉映射
-// ==========================================
 const platformIconMap: Record<string, string> = {
   微博热搜: 'fa-brands fa-weibo',
   微博: 'fa-brands fa-weibo',
@@ -171,9 +162,7 @@ function formatRelativeTime(dateStr: string): string {
   return `${days} 天前`
 }
 
-// ==========================================
+
-// 排名图表配置
-// ==========================================
 function getRankingChartOptions(history: number[], platformColor: string) {
   return {
     series: [{ name: '排名', data: history }],
@@ -182,6 +171,14 @@ function getRankingChartOptions(history: number[], platformColor: string) {
       height: 56,
       sparkline: { enabled: true },
       animations: { enabled: true, easing: 'easeinout' as const, speed: 400 },
+      events: {
+        mounted: (chartContext: any) => {
+          chartContext.el?.querySelector('.apexcharts-svg > title')?.remove()
+        },
+        updated: (chartContext: any) => {
+          chartContext.el?.querySelector('.apexcharts-svg > title')?.remove()
+        }
+      }
     },
     stroke: { curve: 'smooth' as const, width: 2 },
     fill: {
@@ -241,9 +238,6 @@ function platformKey(eventId: number, index: number, prefix: string = ''): strin
   return prefix ? `${prefix}-${eventId}-${index}` : `${eventId}-${index}`
 }
 
-// ==========================================
-// 数据加载
-// ==========================================
 async function loadEvents(append = false) {
   if (!append) {
     loading.value = true
@@ -673,9 +667,6 @@ watch(() => route.query.event, (newId) => {
         </template>
       </div>
 
-      <!-- ==========================================
-           右侧：小组件面板
-      ========================================== -->
       <div class="widgets-column">
 
         <!-- 为你推荐（基于用户关键词的匹配） -->
@@ -838,10 +829,10 @@ watch(() => route.query.event, (newId) => {
               <i class="fa-regular fa-clock"></i>
               最后同步: {{ lastSyncText }}
             </span>
-            <span v-if="stats.error_tasks_today > 0" class="error-count">
+            <!-- <span v-if="stats.error_tasks_today > 0" class="error-count">
               <i class="fa-solid fa-triangle-exclamation"></i>
               {{ stats.error_tasks_today }} 个异常
-            </span>
+            </span> -->
           </div>
         </section>
       </div>
@@ -889,9 +880,6 @@ watch(() => route.query.event, (newId) => {
   margin-top: 6px;
 }
 
-/* ==========================================
-   网格布局
-========================================== */
 .content-grid {
   display: flex;
   flex-direction: column;
@@ -923,9 +911,6 @@ watch(() => route.query.event, (newId) => {
   }
 }
 
-/* ==========================================
-   区域标题 + 热度阈值 (高级磨砂透明风)
-========================================== */
 .section-header {
   margin-bottom: 24px;
 }
@@ -1015,9 +1000,6 @@ watch(() => route.query.event, (newId) => {
   box-shadow: var(--shadow-sm);
 }
 
-/* ==========================================
-   事件卡片
-========================================== */
 /* 事件卡片，加入毛玻璃与高级阴影 */
 .event-card {
   background: var(--bg-surface);
@@ -1134,9 +1116,6 @@ watch(() => route.query.event, (newId) => {
   color: transparent;
 }
 
-/* ==========================================
-   平台列表 + 悬停排名图
-========================================== */
 .platforms-list {
   display: flex;
   flex-direction: column;
@@ -1254,9 +1233,6 @@ watch(() => route.query.event, (newId) => {
   max-height: 120px;
 }
 
-/* ==========================================
-   加载更多
-========================================== */
 .load-more-wrapper {
   display: flex;
   flex-direction: column;
@@ -1302,9 +1278,6 @@ watch(() => route.query.event, (newId) => {
   color: var(--text-placeholder);
 }
 
-/* ==========================================
-   小组件面板（通用）- 玻璃拟态高级质感
-========================================== */
 .widget-panel {
   background: var(--bg-surface);
   backdrop-filter: var(--backdrop-blur);
@@ -1398,9 +1371,6 @@ watch(() => route.query.event, (newId) => {
   font-size: 13px;
 }
 
-/* ==========================================
-   为你推荐面板
-========================================== */
 .recommend-header {
   background: rgba(139, 92, 246, 0.06);
   border-bottom-color: rgba(139, 92, 246, 0.15);
@@ -1576,9 +1546,6 @@ watch(() => route.query.event, (newId) => {
   font-size: 9px;
 }
 
-/* ==========================================
-   公关修改追踪
-========================================== */
 .revision-header {
   background: rgba(239, 68, 68, 0.06);
   border-bottom-color: rgba(239, 68, 68, 0.15);
@@ -1679,9 +1646,6 @@ watch(() => route.query.event, (newId) => {
   margin: 0;
 }
 
-/* ==========================================
-   系统状态
-========================================== */
 .stats-widget {
   padding: 16px;
 }
@@ -1751,9 +1715,6 @@ watch(() => route.query.event, (newId) => {
   color: var(--status-error);
 }
 
-/* ==========================================
-   聚光灯区块
-========================================== */
 .spotlight-wrap {
   margin-bottom: 20px;
 }

frontend/src/views/DeliveryView.vue

@@ -1,4 +1,3 @@
-<!-- 推送设置页：管理推送时间表与推送渠道（邮箱等） -->
 <script setup lang="ts">
 import { onMounted, ref, computed } from 'vue'
 
@@ -62,9 +61,6 @@ async function loadConfig() {
   }
 }
 
-// ==========================================
-// 推送时间表操作
-// ==========================================
 async function handleAddSchedule() {
   if (!userId.value || !newTime.value) return
   submittingSchedule.value = true
@@ -109,9 +105,6 @@ async function handleDeleteSchedule(schedule: DeliverySchedule) {
   }
 }
 
-// ==========================================
-// 推送渠道操作
-// ==========================================
 async function handleAddEndpoint() {
   if (!userId.value || !newChannelAccount.value.trim()) return
   submittingEndpoint.value = true
@@ -186,9 +179,6 @@ onMounted(loadConfig)
     </div>
 
     <div v-else class="config-sections">
-      <!-- ==========================================
-           推送时间管理
-      ========================================== -->
       <section class="config-section">
         <div class="section-title">
           <h2><i class="fa-regular fa-clock"></i> 推送时间</h2>
@@ -229,9 +219,6 @@ onMounted(loadConfig)
         </div>
       </section>
 
-      <!-- ==========================================
-           推送渠道管理
-      ========================================== -->
       <section class="config-section">
         <div class="section-title">
           <h2><i class="fa-solid fa-envelope"></i> 接收邮箱</h2>
@@ -374,9 +361,6 @@ onMounted(loadConfig)
   color: var(--text-secondary);
 }
 
-/* ==========================================
-   通用区块样式
-========================================== */
 .config-sections {
   display: flex;
   flex-direction: column;
@@ -418,9 +402,6 @@ onMounted(loadConfig)
   margin: 0;
 }
 
-/* ==========================================
-   添加行
-========================================== */
 .add-row {
   display: flex;
   gap: 10px;
@@ -497,9 +478,6 @@ onMounted(loadConfig)
   font-size: 13px;
 }
 
-/* ==========================================
-   时间表列表
-========================================== */
 .schedule-list {
   display: flex;
   flex-direction: column;
@@ -573,9 +551,6 @@ onMounted(loadConfig)
   background: rgba(239, 68, 68, 0.1);
 }
 
-/* ==========================================
-   渠道列表
-========================================== */
 .endpoint-add {
   flex-wrap: wrap;
 }
@@ -661,9 +636,6 @@ onMounted(loadConfig)
   gap: 6px;
 }
 
-/* ==========================================
-   工作原理说明
-========================================== */
 .info-section {
   background: transparent;
   border: 1px dashed var(--border-subtle);

frontend/src/views/HomeView.vue

@@ -1,4 +1,3 @@
-<!-- 概览页：展示当前账户、会话状态、认证接入说明 -->
 <script setup lang="ts">
 import { computed } from 'vue'
 import { useRouter } from 'vue-router'
@@ -31,7 +30,7 @@ async function handleLogout() {
         <div class="nav-brand">
           <div class="logo">
             <BrandLogo />
-            InsightRadar
+            聚势智见
           </div>
         </div>
         <div class="nav-actions">

frontend/src/views/LoginView.vue

@@ -1,4 +1,3 @@
-<!-- 登录页：支持密码登录与邮箱验证码登录 -->
 <script setup lang="ts">
 import { computed, onUnmounted, reactive, ref, watch } from 'vue'
 import { useRoute, useRouter } from 'vue-router'
@@ -150,7 +149,7 @@ onUnmounted(() => {
       <div class="brand-content">
         <div class="logo">
           <BrandLogo />
-          InsightRadar
+          聚势智见
         </div>
         <h1 class="brand-title">洞察全网热点<br />让信息更聚焦</h1>
         <p class="brand-desc">
@@ -192,7 +191,7 @@ onUnmounted(() => {
       <div class="form-container">
         <div class="form-header">
           <h2>欢迎回来</h2>
-          <p>登录后继续查看 InsightRadar 实时动态</p>
+          <p>登录后继续查看 聚势智见 实时动态</p>
         </div>
 
         <div class="login-mode-tabs">
@@ -303,9 +302,6 @@ onUnmounted(() => {
 </template>
 
 <style scoped>
-/* ==========================================
-   全新高级分屏布局与背景
-========================================== */
 .split-layout {
   display: flex;
   min-height: 100vh;

frontend/src/views/RegisterView.vue

@@ -1,4 +1,3 @@
-<!-- 注册页：邮箱验证码 + 密码，带密码强度提示 -->
 <script setup lang="ts">
 import { computed, onUnmounted, reactive, ref } from 'vue'
 import { useRouter } from 'vue-router'
@@ -131,7 +130,7 @@ onUnmounted(() => {
       <div class="brand-content">
         <div class="logo">
           <BrandLogo />
-          InsightRadar
+          聚势智见
         </div>
         <h1 class="brand-title">开启智能<br />分析之旅。</h1>
         <p class="brand-desc">
@@ -280,9 +279,6 @@ onUnmounted(() => {
 </template>
 
 <style scoped>
-/* ==========================================
-   全新高级分屏布局与背景
-========================================== */
 .split-layout {
   display: flex;
   min-height: 100vh;

frontend/src/views/RevisionsView.vue

@@ -1,4 +1,3 @@
-<!-- 公关修改追踪页：展示热搜标题被偷偷修改的历史记录 -->
 <script setup lang="ts">
 import { computed, onMounted, ref, reactive } from 'vue'
 

frontend/src/views/SearchView.vue

@@ -1,4 +1,3 @@
-<!-- 事件追踪分析页：关键词搜索、时间热度图表、关联事件列表 -->
 <script setup lang="ts">
 import { ref, computed } from 'vue'
 import VueApexCharts from 'vue3-apexcharts'
@@ -72,6 +71,12 @@ const chartOptions = ref<ApexOptions>({
     },
     // 点击图表数据点：切换选中时间，再次点击则取消筛选
     events: {
+      mounted: (chartContext: any) => {
+        chartContext.el?.querySelector('.apexcharts-svg > title')?.remove()
+      },
+      updated: (chartContext: any) => {
+        chartContext.el?.querySelector('.apexcharts-svg > title')?.remove()
+      },
       markerClick: function(event: unknown, chartContext: unknown, { dataPointIndex }: never) {
         if (searchResult.value && searchResult.value.timeline[dataPointIndex]) {
           const clickedTime = searchResult.value.timeline[dataPointIndex].time_label
@@ -229,17 +234,17 @@ async function handleSearch() {
         <div class="tips-box glass-panel">
           <h2 class="panel-title"><i class="fa-regular fa-lightbulb"></i> 搜索建议</h2>
           <div class="tips-content">
-            <button class="tip-tag" @click="keyword='新能源汽车'; hours=168; handleSearch()">
+            <button class="tip-tag" @click="keyword='火箭发射'; hours=168; handleSearch()">
-              <i class="fa-solid fa-rocket"></i> 新能源汽车
+              <i class="fa-solid fa-rocket"></i> 火箭发射
             </button>
             <button class="tip-tag" @click="keyword='苹果公司'; hours=168; handleSearch()">
-              <i class="fa-brands fa-apple"></i> 苹果产业链
+              <i class="fa-brands fa-apple"></i> 苹果公司
             </button>
             <button class="tip-tag regex-tag" @click="keyword='AI|LLM'; hours=168; handleSearch()">
               <i class="fa-solid fa-code-branch"></i> AI / 大模型
             </button>
-            <button class="tip-tag regex-tag" @click="keyword='美国关税'; hours=168; handleSearch()">
+            <button class="tip-tag regex-tag" @click="keyword='美国'; hours=168; handleSearch()">
-              <i class="fa-solid fa-flag-usa"></i> 美国关税
+              <i class="fa-solid fa-flag-usa"></i> 美国
             </button>
           </div>
         </div>
@@ -255,9 +260,15 @@ async function handleSearch() {
       <div v-else-if="searchResult" class="results-container">
         <section class="chart-section glass-panel">
           <div class="section-header">
+            <div class="section-title-group">
               <h2 class="section-title">
                 <i class="fa-solid fa-wave-square"></i> 时间热度脉络
               </h2>
+              <span class="chart-tip">
+                <i class="fa-solid fa-hand-pointer"></i>
+                点击时间点查看具体事件列表
+              </span>
+            </div>
             <span class="meta-info">共 {{ searchResult.timeline.length }} 个时间节点 · 覆盖 {{ searchResult.events.length }} 个聚合事件</span>
           </div>
 
@@ -547,6 +558,30 @@ async function handleSearch() {
   color: var(--brand-primary);
 }
 
+.section-title-group {
+  display: flex;
+  align-items: center;
+  gap: 12px;
+  flex-wrap: wrap;
+}
+
+.chart-tip {
+  display: inline-flex;
+  align-items: center;
+  gap: 6px;
+  padding: 4px 10px;
+  border-radius: var(--radius-md);
+  background: var(--brand-primary-alpha);
+  border: 1px solid rgba(99, 102, 241, 0.2);
+  color: var(--brand-primary);
+  font-size: 12px;
+  font-weight: 600;
+}
+
+.chart-tip i {
+  font-size: 12px;
+}
+
 .time-filter-badge {
   display: inline-flex;
   align-items: center;
@@ -585,7 +620,16 @@ async function handleSearch() {
 
 .chart-container {
   margin-top: 16px;
-  margin-left: -10px; /* 视觉上抵消 apexcharts 的默认左侧留白。 */
+  margin-left: -10px;
+}
+
+.chart-container :deep(svg),
+.chart-container :deep(canvas) {
+  outline: none;
+}
+
+.chart-container :deep(.apexcharts-marker) {
+  cursor: pointer;
 }
 
 .events-section {
@@ -595,7 +639,6 @@ async function handleSearch() {
 .events-grid {
   display: flex;
   flex-direction: column;
-  /* 与 DashboardView 保持一致，列表按纵向堆叠展示。 */
 }
 
 .loading-state {

frontend/src/views/TopicsView.vue

@@ -1,4 +1,3 @@
-<!-- 兴趣关键词页：添加/删除关键词，查看命中事件 -->
 <script setup lang="ts">
 import { onMounted, ref, computed } from 'vue'
 
@@ -156,7 +155,7 @@ onMounted(async () => {
             v-model="newKeyword"
             type="text"
             class="keyword-input"
-            placeholder="输入关键词，如「直升机」「科比」「佐巴扬」..."
+            placeholder="输入关键词，如「篮球」「科比」「科技」..."
             maxlength="100"
             @keydown="onInputKeydown"
           />