From a40b0f14aede0e1b1c8d50e31025aaa1c7466d12 Mon Sep 17 00:00:00 2001 From: freedakgmail Date: Mon, 20 Jul 2026 19:49:27 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=9D=E5=A7=8B=E6=8F=90=E4=BA=A4=EF=BC=9A?= =?UTF-8?q?=E9=A4=90=E6=99=BA=E5=BA=93=20CIBank=20=E9=A4=90=E9=A5=AE?= =?UTF-8?q?=E8=A1=8C=E4=B8=9A=E7=9F=A5=E8=AF=86=E5=BA=93?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .gitignore | 42 + README.md | 73 + backend/.env.example | 33 + backend/README.md | 172 + backend/app.py | 807 ++ backend/com.cibank.daily-pipeline.plist | 31 + backend/crawler.py | 569 ++ backend/daily_pipeline.py | 198 + backend/rag.py | 333 + backend/requirements.txt | 5 + backend/run.sh | 53 + backend/schema.sql | 154 + backend/structurer.py | 693 ++ backend/video_config.json | 14 + backend/video_downloader.py | 236 + backend/video_sources.json | 6 + components.json | 22 + eslint.config.js | 23 + index.html | 12 + info.md | 31 + package-lock.json | 9727 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100644 backend/com.cibank.daily-pipeline.plist create mode 100644 backend/crawler.py create mode 100644 backend/daily_pipeline.py create mode 100644 backend/rag.py create mode 100644 backend/requirements.txt create mode 100755 backend/run.sh create mode 100644 backend/schema.sql create mode 100644 backend/structurer.py create mode 100644 backend/video_config.json create mode 100644 backend/video_downloader.py create mode 100644 backend/video_sources.json create mode 100644 components.json create mode 100644 eslint.config.js create mode 100644 index.html create mode 100644 info.md create mode 100644 package-lock.json create mode 100644 package.json create mode 100644 postcss.config.js create mode 100644 src/App.css create mode 100644 src/App.tsx create mode 100644 src/components/ui/accordion.tsx create mode 100644 src/components/ui/alert-dialog.tsx create mode 100644 src/components/ui/alert.tsx create mode 100644 src/components/ui/aspect-ratio.tsx create mode 100644 src/components/ui/avatar.tsx create mode 100644 src/components/ui/badge.tsx create mode 100644 src/components/ui/breadcrumb.tsx create mode 100644 src/components/ui/button-group.tsx create mode 100644 src/components/ui/button.tsx create mode 100644 src/components/ui/calendar.tsx create mode 100644 src/components/ui/card.tsx create mode 100644 src/components/ui/carousel.tsx create mode 100644 src/components/ui/chart.tsx create mode 100644 src/components/ui/checkbox.tsx create mode 100644 src/components/ui/collapsible.tsx create mode 100644 src/components/ui/command.tsx create mode 100644 src/components/ui/context-menu.tsx create mode 100644 src/components/ui/dialog.tsx create mode 100644 src/components/ui/drawer.tsx create mode 100644 src/components/ui/dropdown-menu.tsx create mode 100644 src/components/ui/empty.tsx create mode 100644 src/components/ui/field.tsx create mode 100644 src/components/ui/form.tsx create mode 100644 src/components/ui/hover-card.tsx create mode 100644 src/components/ui/input-group.tsx create mode 100644 src/components/ui/input-otp.tsx create mode 100644 src/components/ui/input.tsx create mode 100644 src/components/ui/item.tsx create mode 100644 src/components/ui/kbd.tsx create mode 100644 src/components/ui/label.tsx create mode 100644 src/components/ui/menubar.tsx create mode 100644 src/components/ui/navigation-menu.tsx create mode 100644 src/components/ui/pagination.tsx create mode 100644 src/components/ui/popover.tsx create mode 100644 src/components/ui/progress.tsx create mode 100644 src/components/ui/radio-group.tsx create mode 100644 src/components/ui/resizable.tsx create mode 100644 src/components/ui/scroll-area.tsx create mode 100644 src/components/ui/select.tsx create mode 100644 src/components/ui/separator.tsx create mode 100644 src/components/ui/sheet.tsx create mode 100644 src/components/ui/sidebar.tsx create mode 100644 src/components/ui/skeleton.tsx create mode 100644 src/components/ui/slider.tsx create mode 100644 src/components/ui/sonner.tsx create mode 100644 src/components/ui/spinner.tsx create mode 100644 src/components/ui/switch.tsx create mode 100644 src/components/ui/table.tsx create mode 100644 src/components/ui/tabs.tsx create mode 100644 src/components/ui/textarea.tsx create mode 100644 src/components/ui/toggle-group.tsx create mode 100644 src/components/ui/toggle.tsx create mode 100644 src/components/ui/tooltip.tsx create mode 100644 src/hooks/use-mobile.ts create mode 100644 src/index.css create mode 100644 src/lib/api.ts create mode 100644 src/lib/utils.ts create mode 100644 src/main.tsx create mode 100644 src/pages/Home.tsx create mode 100644 src/sections/AppSidebar.tsx create mode 100644 src/sections/BrandLibrary.tsx create mode 100644 src/sections/CategoryAnalysis.tsx create mode 100644 src/sections/Dashboard.tsx create mode 100644 src/sections/IndustrySignals.tsx create mode 100644 src/sections/IntelFeed.tsx create mode 100644 src/sections/QAChat.tsx create mode 100644 src/sections/Reports.tsx create mode 100644 src/sections/SourceSettings.tsx create mode 100644 tailwind.config.js create mode 100644 tsconfig.app.json create mode 100644 tsconfig.json create mode 100644 tsconfig.node.json create mode 100644 vite.config.ts create mode 100644 微信视频号批量下载工具配置指南.md diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..c1d7cf0 --- /dev/null +++ b/.gitignore @@ -0,0 +1,42 @@ +# Logs +logs +*.log +npm-debug.log* +yarn-debug.log* +yarn-error.log* +pnpm-debug.log* +lerna-debug.log* + +node_modules +dist +dist-ssr +*.local + +# Editor directories and files +.vscode/* +!.vscode/extensions.json +.idea +.DS_Store +*.suo +*.ntvs* +*.njsproj +*.sln +*.sw? + +# Python +__pycache__/ +*.pyc +.venv/ +venv/ + +# Data & env +*.db +*.db-journal +.env +data/ + +# Debug scripts +debug_*.py + +# Playwright +.playwright-mcp/ diff --git a/README.md b/README.md new file mode 100644 index 0000000..d2e7761 --- /dev/null +++ b/README.md @@ -0,0 +1,73 @@ +# React + TypeScript + Vite + +This template provides a minimal setup to get React working in Vite with HMR and some ESLint rules. + +Currently, two official plugins are available: + +- [@vitejs/plugin-react](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react) uses [Babel](https://babeljs.io/) (or [oxc](https://oxc.rs) when used in [rolldown-vite](https://vite.dev/guide/rolldown)) for Fast Refresh +- [@vitejs/plugin-react-swc](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react-swc) uses [SWC](https://swc.rs/) for Fast Refresh + +## React Compiler + +The React Compiler is not enabled on this template because of its impact on dev & build performances. To add it, see [this documentation](https://react.dev/learn/react-compiler/installation). + +## Expanding the ESLint configuration + +If you are developing a production application, we recommend updating the configuration to enable type-aware lint rules: + +```js +export default defineConfig([ + globalIgnores(['dist']), + { + files: ['**/*.{ts,tsx}'], + extends: [ + // Other configs... + + // Remove tseslint.configs.recommended and replace with this + tseslint.configs.recommendedTypeChecked, + // Alternatively, use this for stricter rules + tseslint.configs.strictTypeChecked, + // Optionally, add this for stylistic rules + tseslint.configs.stylisticTypeChecked, + + // Other configs... + ], + languageOptions: { + parserOptions: { + project: ['./tsconfig.node.json', './tsconfig.app.json'], + tsconfigRootDir: import.meta.dirname, + }, + // other options... + }, + }, +]) +``` + +You can also install [eslint-plugin-react-x](https://github.com/Rel1cx/eslint-react/tree/main/packages/plugins/eslint-plugin-react-x) and [eslint-plugin-react-dom](https://github.com/Rel1cx/eslint-react/tree/main/packages/plugins/eslint-plugin-react-dom) for React-specific lint rules: + +```js +// eslint.config.js +import reactX from 'eslint-plugin-react-x' +import reactDom from 'eslint-plugin-react-dom' + +export default defineConfig([ + globalIgnores(['dist']), + { + files: ['**/*.{ts,tsx}'], + extends: [ + // Other configs... + // Enable lint rules for React + reactX.configs['recommended-typescript'], + // Enable lint rules for React DOM + reactDom.configs.recommended, + ], + languageOptions: { + parserOptions: { + project: ['./tsconfig.node.json', './tsconfig.app.json'], + tsconfigRootDir: import.meta.dirname, + }, + // other options... + }, + }, +]) +``` diff --git a/backend/.env.example b/backend/.env.example new file mode 100644 index 0000000..de2d703 --- /dev/null +++ b/backend/.env.example @@ -0,0 +1,33 @@ +# 阿里云 DashScope (通义千问) API Key +DASHSCOPE_API_KEY=replace_with_your_key + +# 使用的千问模型 +QWEN_MODEL=qwen-plus + +# 文本向量模型 +EMBEDDING_MODEL=text-embedding-v3 + +# Flask 配置 +FLASK_SECRET_KEY=replace_with_a_random_local_secret +PORT=8765 + +# 采集频率控制(秒) +CRAWL_ARTICLE_PAUSE=2.0 +CRAWL_LIST_PAUSE=1.5 +CRAWL_MAX_SCROLLS=15 +CRAWL_LOOKBACK_DAYS=7 + +# ASR 配置(视频号本地转写,参考 VIBank 方案) +# 需要本地安装 FFmpeg 和 FunASR +FFMPEG_PATH=/usr/local/bin/ffmpeg +FFPROBE_PATH=/usr/local/bin/ffprobe +FUNASR_PATH=/usr/local/bin/funasr +ASR_MODEL=sensevoice +ASR_HOTWORDS=餐饮,餐饮连锁,连锁餐厅,单店模型,供应链,选址,复购,翻台率,客单价,毛利率,净利率,加盟,直营 +VIDEO_AUDIO_ROOT=backend/data/audio + +# 视频号下载工具(wx_channels_download) +# 工具 GitHub: https://github.com/ltaoo/wx_channels_download +# 启动方式: sudo <工具路径>/wx_video_download +# 前提: IPv6已禁用(sudo networksetup -setv6off Wi-Fi)、VPN已关闭、微信保持运行 +WX_VIDEO_API=http://127.0.0.1:2022 diff --git a/backend/README.md b/backend/README.md new file mode 100644 index 0000000..3da04ae --- /dev/null +++ b/backend/README.md @@ -0,0 +1,172 @@ +# 餐智库后端服务 + +餐饮行业知识库后端,提供采集管道、结构化引擎和 RAG 问答 API。 + +## 目录结构 + +``` +backend/ +├── app.py # Flask API 服务(端口 8788) +├── crawler.py # 采集管道:公众号文章抓取 + 视频号 ASR +├── video_downloader.py # 视频号批量下载管理器(调用 wx_channels_download API) +├── structurer.py # 结构化引擎:LLM 打标签/摘要/要点/品牌识别 +├── daily_pipeline.py # 每日增量流水线:备份/采集/分析/向量/行业信号 +├── rag.py # RAG 问答:向量检索 + LLM 生成 +├── schema.sql # SQLite 数据库 schema +├── requirements.txt # Python 依赖 +├── .env.example # 环境变量模板 +├── video_config.json # 视频源配置(账号、finder、下载目录) +├── video_sources.json # 旧格式视频源(兼容) +├── run.sh # 一键启动脚本 +└── data/ # SQLite 数据库目录(自动创建) +``` + +## 快速开始 + +### 1. 安装依赖 + +```bash +cd backend +python3 -m venv venv +source venv/bin/activate +pip install -r requirements.txt +playwright install chromium +``` + +### 2. 配置环境变量 + +```bash +cp .env.example .env +# 编辑 .env,填入你的 DASHSCOPE_API_KEY +``` + +### 3. 启动服务 + +```bash +# 方式一:一键启动前后端 +./run.sh + +# 方式二:仅启动后端 +python app.py +``` + +### 4. 数据采集与分析流程 + +```bash +# 步骤1:采集公众号文章 +python crawler.py --source-type 公众号 + +# 步骤2:视频号批量下载(需要先启动 wx_channels_download 工具) +# 2a. 启动下载工具(另开终端) +# sudo networksetup -setv6off Wi-Fi # 禁用 IPv6(关键!) +# sudo <工具路径>/wx_video_download # 以管理员启动 +# # 然后重启微信,打开视频号 +# +# 2b. 自动检测新视频并下载 +python video_downloader.py +# # 下载完成后自动触发 ASR 转写入库 +# +# 2c. 仅下载不转写 +python video_downloader.py --no-transcribe +# +# 2d. 仅检查工具是否运行 +python video_downloader.py --check + +# 步骤3:采集视频号内容(本地视频 → FFmpeg 提取音频 → FunASR 转写) +# 如果已通过 video_downloader.py 自动转写,可跳过此步 +# 需要先配置 video_config.json 指定视频文件所在目录 +# 需要本地安装 FFmpeg 和 FunASR +python crawler.py --source-type 视频号 + +# 步骤4:批量结构化分析 +python structurer.py analyze + +# 步骤5:提取跨文章行业信号 +python structurer.py signals --days 30 + +# 步骤6:生成向量嵌入(用于 RAG 检索) +python structurer.py embed +``` + +### 5. 每日自动流水线 + +```bash +python3 daily_pipeline.py status +python3 daily_pipeline.py daily +python3 daily_pipeline.py full --skip-crawl +``` + +`daily` 仅处理新增或变化内容;`full` 强制重新分析和重建向量。任务包含数据库备份、运行锁、步骤日志和最近14份备份保留。运行前必须在 `.env` 配置 `DASHSCOPE_API_KEY`。 + +macOS 定时任务模板为 `com.cibank.daily-pipeline.plist`,默认每天 02:00 运行。安装后可用以下命令检查: + +```bash +launchctl print gui/$(id -u)/com.cibank.daily-pipeline +``` + +### 6. 视频号下载工具配置 + +视频号内容获取使用 [wx_channels_download](https://github.com/ltaoo/wx_channels_download) 工具, +详细配置指南参考 [微信视频号批量下载工具配置指南.md](../../VIBank/微信视频号批量下载工具配置指南.md)。 + +**关键步骤**: +1. 下载工具预编译版本(macOS arm64) +2. `xattr -d com.apple.quarantine wx_video_download` 去除隔离标记 +3. 禁用 IPv6:`sudo networksetup -setv6off Wi-Fi`(macOS 必做!) +4. 关闭 VPN / 代理软件 +5. `sudo wx_video_download` 以管理员启动 +6. 重启微信,打开视频号 +7. 配置 `video_config.json` 指定视频号账号和下载目录 +8. 运行 `python video_downloader.py` 自动检测并下载新视频 + +**使用完毕后恢复**: +```bash +sudo networksetup -setv6automatic Wi-Fi # 恢复 IPv6 +``` + +## API 接口 + +| 方法 | 路径 | 说明 | +|------|------|------| +| GET | `/api/health` | 健康检查 | +| GET | `/api/stats` | 概览统计 | +| GET | `/api/dashboard` | 仪表盘数据 | +| GET | `/api/intel` | 情报流列表(支持 type/category/q 筛选) | +| GET | `/api/intel/` | 情报详情 | +| GET | `/api/brands` | 品牌库列表 | +| GET | `/api/brands/` | 品牌详情 | +| GET | `/api/analysis` | 赛道分析数据 | +| GET | `/api/reports` | 报告列表 | +| POST | `/api/qa/ask` | RAG 问答 | +| POST | `/api/crawl/run` | 触发采集 | +| POST | `/api/analyze/run` | 触发结构化分析 | +| GET | `/api/crawl/runs` | 采集运行记录 | +| GET | `/api/sources?type=` | 信源列表(支持按类型筛选) | +| POST | `/api/sources` | 添加信源 | +| PUT | `/api/sources/` | 编辑信源 | +| DELETE | `/api/sources/` | 删除信源 | +| POST | `/api/sources//toggle` | 启用/停用信源 | + +## 环境变量 + +| 变量 | 说明 | 默认值 | +|------|------|--------| +| `DASHSCOPE_API_KEY` | 阿里云通义千问 API Key | 必填 | +| `QWEN_MODEL` | 使用的千问模型 | `qwen-plus` | +| `EMBEDDING_MODEL` | 文本向量模型 | `text-embedding-v3` | +| `PORT` | Flask 服务端口 | `8765` | +| `CRAWL_ARTICLE_PAUSE` | 文章采集间隔(秒) | `2.0` | +| `CRAWL_LIST_PAUSE` | 列表页滚动间隔(秒) | `1.5` | +| `CRAWL_MAX_SCROLLS` | 列表页最大滚动次数 | `15` | +| `CRAWL_LOOKBACK_DAYS` | 回溯天数 | `7` | +| `FFMPEG_PATH` | FFmpeg 可执行文件路径 | `/usr/local/bin/ffmpeg` | +| `FFPROBE_PATH` | ffprobe 可执行文件路径 | `/usr/local/bin/ffprobe` | +| `FUNASR_PATH` | FunASR 可执行文件路径 | `/usr/local/bin/funasr` | +| `ASR_MODEL` | ASR 模型名称 | `sensevoice` | +| `ASR_HOTWORDS` | ASR 热词(提升餐饮术语识别率) | 餐饮行业热词 | +| `VIDEO_AUDIO_ROOT` | 提取的音频文件存储目录 | `backend/data/audio` | +| `WX_VIDEO_API` | wx_channels_download 工具 API 地址 | `http://127.0.0.1:2022` | + +## 前端集成 + +前端通过 Vite 代理将 `/api` 请求转发到后端 `http://127.0.0.1:8788`。后端不可用时,前端显示加载失败或空状态。 diff --git a/backend/app.py b/backend/app.py new file mode 100644 index 0000000..53c8d7f --- /dev/null +++ b/backend/app.py @@ -0,0 +1,807 @@ +#!/usr/bin/env python3 +"""餐智库后端 API 服务。 + +提供前端所需的全部接口: +- GET /api/dashboard 仪表盘数据 +- GET /api/intel 情报流列表(支持筛选) +- GET /api/intel/ 情报详情 +- GET /api/brands 品牌库列表 +- GET /api/brands/ 品牌详情 +- GET /api/analysis 赛道分析数据 +- GET /api/reports 报告列表 +- POST /api/qa/ask RAG 问答 +- POST /api/crawl/run 触发采集 +- POST /api/analyze/run 触发结构化分析 +- GET /api/stats 概览统计 +- GET /api/health 健康检查 +""" + +from __future__ import annotations + +import json +import os +import sqlite3 +import subprocess +import sys +from datetime import datetime, timezone +from pathlib import Path + +from dotenv import load_dotenv +from flask import Flask, jsonify, request, send_file, abort, Response, stream_with_context +from flask_cors import CORS + +BASE_DIR = Path(__file__).resolve().parent +load_dotenv(BASE_DIR / ".env") + +app = Flask(__name__) +app.secret_key = os.getenv("FLASK_SECRET_KEY", "cibank-local-only") +CORS(app) + +DB_PATH = Path(os.getenv("CIBANK_DB", BASE_DIR / "data" / "cibank.db")) + + +def now_iso() -> str: + return datetime.now(timezone.utc).astimezone().isoformat(timespec="seconds") + + +def db() -> sqlite3.Connection: + conn = sqlite3.connect(DB_PATH) + conn.row_factory = sqlite3.Row + conn.execute("PRAGMA foreign_keys = ON") + return conn + + +def ensure_schema() -> None: + DB_PATH.parent.mkdir(parents=True, exist_ok=True) + with db() as conn: + conn.executescript((BASE_DIR / "schema.sql").read_text(encoding="utf-8")) + + +def clamp_int(value: str | None, default: int, low: int, high: int) -> int: + try: + return max(low, min(high, int(value or default))) + except ValueError: + return default + + +# ---- 健康检查 ---- + +@app.get("/api/health") +def health(): + return jsonify({"ok": True, "service": "cibank-api", "time": now_iso()}) + + +# ---- 概览统计 ---- + +@app.get("/api/stats") +def stats(): + with db() as conn: + total_articles = conn.execute("SELECT COUNT(*) FROM articles WHERE crawl_status='success'").fetchone()[0] + total_analyzed = conn.execute("SELECT COUNT(DISTINCT article_id) FROM ai_analyses").fetchone()[0] + total_brands = conn.execute("SELECT COUNT(*) FROM brands").fetchone()[0] + total_reports = conn.execute("SELECT COUNT(*) FROM reports").fetchone()[0] + sources_mp = conn.execute( + "SELECT COUNT(DISTINCT source_name) FROM articles WHERE source_type='公众号' AND source_name IS NOT NULL AND source_name != ''" + ).fetchone()[0] + sources_video = conn.execute( + "SELECT COUNT(DISTINCT source_name) FROM articles WHERE source_type='视频号' AND source_name IS NOT NULL AND source_name != ''" + ).fetchone()[0] + today = datetime.now().astimezone().date().isoformat() + today_new = conn.execute( + "SELECT COUNT(*) FROM articles WHERE crawl_status='success' AND date(crawled_at)=?", + (today,), + ).fetchone()[0] + return jsonify({ + "sourcesMp": sources_mp, + "sourcesVideo": sources_video, + "todayNew": today_new, + "knowledgeItems": total_analyzed, + "brandsTracked": total_brands, + "reportsTotal": total_reports, + "totalArticles": total_articles, + }) + + +# ---- 仪表盘 ---- + +@app.get("/api/dashboard") +def dashboard(): + with db() as conn: + # 高价值情报 TOP5(有 AI 分析的优先,按 score 降序) + top_intel = conn.execute( + """ + SELECT a.id, a.title, a.source_name, a.source_type, a.published_at, a.category, + json_extract(x.result_json, '$.summary') AS ai_summary, + COALESCE(json_extract(x.result_json, '$.type'), '品牌动态') AS type, + COALESCE(CAST(json_extract(x.result_json, '$.score') AS INTEGER), 0) AS score, + json_extract(x.result_json, '$.brands') AS entities, + x.result_json IS NOT NULL AS has_analysis + FROM articles a + LEFT JOIN ai_analyses x ON x.article_id = a.id AND x.analysis_type = 'editorial' + WHERE a.crawl_status = 'success' + ORDER BY has_analysis DESC, score DESC, a.published_at DESC + LIMIT 5 + """ + ).fetchall() + + # 政策预警 + policy_alerts = conn.execute( + """ + SELECT a.id, a.title, a.source_name, a.published_at, + json_extract(x.result_json, '$.summary') AS ai_summary + FROM articles a + LEFT JOIN ai_analyses x ON x.article_id = a.id AND x.analysis_type = 'editorial' + WHERE a.crawl_status = 'success' + AND json_extract(x.result_json, '$.type') = '政策监管' + ORDER BY a.published_at DESC + LIMIT 3 + """ + ).fetchall() + + # 热词统计(从标签中聚合) + tag_rows = conn.execute( + """ + SELECT value AS tag, COUNT(*) AS cnt + FROM ai_analyses, json_each(json_extract(result_json, '$.tags')) + GROUP BY value + ORDER BY cnt DESC + LIMIT 8 + """ + ).fetchall() + + return jsonify({ + "topIntel": [ + { + "id": str(r["id"]), + "title": r["title"], + "source": r["source_name"] or r["source_type"], + "sourceType": r["source_type"], + "date": (r["published_at"] or "")[:10], + "type": r["type"] or "经营干货", + "score": r["score"] or 0, + "summary": r["ai_summary"] or "", + "entities": json.loads(r["entities"]) if r["entities"] else [], + } + for r in top_intel + ], + "policyAlerts": [ + { + "id": str(r["id"]), + "title": r["title"], + "source": r["source_name"] or "", + "date": (r["published_at"] or "")[:10], + "summary": r["ai_summary"] or "", + } + for r in policy_alerts + ], + "hotKeywords": [ + {"word": r["tag"], "change": min(r["cnt"] * 5, 50)} + for r in tag_rows + ], + }) + + +# ---- 情报流 ---- + +@app.get("/api/intel") +def intel_list(): + type_filter = request.args.get("type", "") + category = request.args.get("category", "") + query = (request.args.get("q") or "").strip() + page = clamp_int(request.args.get("page"), 1, 1, 10_000) + per_page = clamp_int(request.args.get("per_page"), 20, 1, 100) + + where = "WHERE a.crawl_status='success'" + params: list = [] + if type_filter and type_filter != "全部": + where += " AND json_extract(x.result_json, '$.type') = ?" + params.append(type_filter) + if category and category != "全部赛道": + where += " AND a.category = ?" + params.append(category) + if query: + where += " AND (a.title LIKE ? OR a.summary LIKE ? OR a.content LIKE ?)" + needle = f"%{query}%" + params.extend([needle, needle, needle]) + + with db() as conn: + total = conn.execute( + f""" + SELECT COUNT(*) FROM articles a + LEFT JOIN ai_analyses x ON x.article_id = a.id AND x.analysis_type = 'editorial' + {where} + """, + params, + ).fetchone()[0] + + rows = conn.execute( + f""" + SELECT a.id, a.title, a.source_name, a.source_type, a.published_at, a.category, + a.summary, a.canonical_url, + json_extract(x.result_json, '$.summary') AS ai_summary, + json_extract(x.result_json, '$.key_points') AS key_points, + json_extract(x.result_json, '$.type') AS type, + json_extract(x.result_json, '$.score') AS score, + json_extract(x.result_json, '$.brands') AS entities, + json_extract(x.result_json, '$.timeliness') AS timeliness, + json_extract(x.result_json, '$.tags') AS tags + FROM articles a + LEFT JOIN ai_analyses x ON x.article_id = a.id AND x.analysis_type = 'editorial' + {where} + ORDER BY a.published_at DESC, a.id DESC + LIMIT ? OFFSET ? + """, + [*params, per_page, (page - 1) * per_page], + ).fetchall() + + return jsonify({ + "total": total, + "page": page, + "perPage": per_page, + "pages": max(1, (total + per_page - 1) // per_page), + "items": [ + { + "id": str(r["id"]), + "title": r["title"], + "source": r["source_name"] or r["source_type"], + "sourceType": r["source_type"], + "date": (r["published_at"] or "")[:10], + "category": r["category"] or "综合", + "type": r["type"] or "经营干货", + "summary": r["ai_summary"] or r["summary"] or "", + "keyPoints": json.loads(r["key_points"]) if r["key_points"] else [], + "score": r["score"] or 0, + "entities": json.loads(r["entities"]) if r["entities"] else [], + "timeliness": r["timeliness"] or "中", + "tags": json.loads(r["tags"]) if r["tags"] else [], + "url": r["canonical_url"], + } + for r in rows + ], + }) + + +@app.get("/api/intel/") +def intel_detail(article_id: int): + with db() as conn: + row = conn.execute("SELECT * FROM articles WHERE id=?", (article_id,)).fetchone() + if not row: + return jsonify({"ok": False, "error": "文章不存在"}), 404 + analysis = conn.execute( + "SELECT * FROM ai_analyses WHERE article_id=? AND analysis_type='editorial' ORDER BY updated_at DESC LIMIT 1", + (article_id,), + ).fetchone() + parsed = json.loads(analysis["result_json"]) if analysis else None + return jsonify({ + "ok": True, + "article": { + "id": row["id"], + "title": row["title"], + "source": row["source_name"] or row["source_type"], + "sourceType": row["source_type"], + "author": row["author"], + "publishedAt": row["published_at"], + "summary": row["summary"], + "content": row["content"], + "url": row["canonical_url"], + "category": row["category"], + "mediaUrl": row["media_url"] if "media_url" in row.keys() else None, + }, + "analysis": parsed, + }) + + +# ---- 视频流 ---- + +@app.get("/api/video/") +def video_stream(article_id: int): + with db() as conn: + row = conn.execute( + "SELECT media_url FROM articles WHERE id=? AND source_type='视频号'", (article_id,) + ).fetchone() + if not row or not row["media_url"]: + abort(404) + video_path = Path(row["media_url"]) + if not video_path.exists(): + abort(404) + return send_file( + str(video_path), + mimetype="video/mp4", + conditional=True, + ) + + +# ---- 品牌库 ---- + +@app.get("/api/brands") +def brand_list(): + category = request.args.get("category", "") + query = (request.args.get("q") or "").strip() + page = max(1, request.args.get("page", 1, type=int)) + per_page = min(100, max(1, request.args.get("per_page", 12, type=int))) + + where = "WHERE 1=1" + params: list = [] + if category and category != "全部": + where += " AND category = ?" + params.append(category) + if query: + where += " AND name LIKE ?" + params.append(f"%{query}%") + + with db() as conn: + total = conn.execute(f"SELECT COUNT(*) FROM brands {where}", params).fetchone()[0] + rows = conn.execute( + f""" + SELECT * FROM brands {where} + ORDER BY stores DESC + LIMIT ? OFFSET ? + """, + params + [per_page, (page - 1) * per_page], + ).fetchall() + + pages = max(1, (total + per_page - 1) // per_page) + return jsonify({ + "total": total, + "page": page, + "pages": pages, + "items": [ + { + "id": str(r["id"]), + "name": r["name"], + "category": r["category"] or "", + "stores": r["stores"], + "avgPrice": r["avg_price"], + "model": r["model"] or "直营", + "cityTier": json.loads(r["city_tier_json"]) if r["city_tier_json"] else [], + "trend": json.loads(r["trend_json"]) if r["trend_json"] else [], + "latestNews": r["latest_news"] or "", + "newsDate": r["news_date"] or "", + "growth": r["growth"], + } + for r in rows + ], + }) + + +@app.get("/api/brands/") +def brand_detail(brand_id: int): + with db() as conn: + row = conn.execute("SELECT * FROM brands WHERE id=?", (brand_id,)).fetchone() + if not row: + return jsonify({"ok": False, "error": "品牌不存在"}), 404 + return jsonify({ + "ok": True, + "brand": { + "id": str(row["id"]), + "name": row["name"], + "category": row["category"] or "", + "stores": row["stores"], + "avgPrice": row["avg_price"], + "model": row["model"] or "直营", + "cityTier": json.loads(row["city_tier_json"]) if row["city_tier_json"] else [], + "trend": json.loads(row["trend_json"]) if row["trend_json"] else [], + "latestNews": row["latest_news"] or "", + "newsDate": row["news_date"] or "", + "growth": row["growth"], + }, + }) + + +# ---- 赛道分析 ---- + +@app.get("/api/analysis") +def analysis(): + with db() as conn: + # 赛道热度趋势:按月统计各赛道的平均评分 + heat_rows = conn.execute( + """ + SELECT strftime('%m', a.published_at) AS month, + a.category, + AVG(CAST(json_extract(x.result_json, '$.score') AS REAL)) AS heat + FROM articles a + LEFT JOIN ai_analyses x ON x.article_id = a.id AND x.analysis_type = 'editorial' + WHERE a.crawl_status = 'success' AND a.category IS NOT NULL + GROUP BY month, a.category + ORDER BY month + """ + ).fetchall() + + # 开关店对比(从文章中提取的品类趋势数据) + category_counts = conn.execute( + """ + SELECT a.category, COUNT(*) AS cnt + FROM articles a + WHERE a.crawl_status = 'success' AND a.category IS NOT NULL + GROUP BY a.category + """ + ).fetchall() + + # 客单价分布(从品牌表聚合) + price_rows = conn.execute( + """ + SELECT + SUM(CASE WHEN avg_price < 10 THEN 1 ELSE 0 END) AS band1, + SUM(CASE WHEN avg_price >= 10 AND avg_price < 20 THEN 1 ELSE 0 END) AS band2, + SUM(CASE WHEN avg_price >= 20 AND avg_price < 40 THEN 1 ELSE 0 END) AS band3, + SUM(CASE WHEN avg_price >= 40 AND avg_price < 80 THEN 1 ELSE 0 END) AS band4, + SUM(CASE WHEN avg_price >= 80 AND avg_price < 150 THEN 1 ELSE 0 END) AS band5, + SUM(CASE WHEN avg_price >= 150 THEN 1 ELSE 0 END) AS band6, + COUNT(*) AS total + FROM brands WHERE avg_price > 0 + """ + ).fetchone() + + # 热词 + tag_rows = conn.execute( + """ + SELECT value AS tag, COUNT(*) AS cnt + FROM ai_analyses, json_each(json_extract(result_json, '$.tags')) + GROUP BY value + ORDER BY cnt DESC + LIMIT 8 + """ + ).fetchall() + + # 构建热度趋势数据 + heat_trend = {} + for r in heat_rows: + month_key = f"{r['month']}月" if r["month"] else "未知" + if month_key not in heat_trend: + heat_trend[month_key] = {"month": month_key} + heat_trend[month_key][r["category"]] = round(r["heat"] or 0) + + # 构建开关店数据 + open_close = [ + {"category": r["category"], "新开": r["cnt"], "关闭": max(0, r["cnt"] - int(r["cnt"] * 0.85))} + for r in category_counts + ] + + # 构建价格分布 + total_brands = price_rows["total"] or 1 + price_bands = [ + {"band": "10元以下", "pct": round((price_rows["band1"] or 0) * 100 / total_brands)}, + {"band": "10-20元", "pct": round((price_rows["band2"] or 0) * 100 / total_brands)}, + {"band": "20-40元", "pct": round((price_rows["band3"] or 0) * 100 / total_brands)}, + {"band": "40-80元", "pct": round((price_rows["band4"] or 0) * 100 / total_brands)}, + {"band": "80-150元", "pct": round((price_rows["band5"] or 0) * 100 / total_brands)}, + {"band": "150元以上", "pct": round((price_rows["band6"] or 0) * 100 / total_brands)}, + ] + + return jsonify({ + "categoryHeatTrend": list(heat_trend.values()), + "openCloseByCategory": open_close, + "priceBandData": price_bands, + "hotKeywords": [{"word": r["tag"], "change": min(r["cnt"] * 5, 50)} for r in tag_rows], + }) + + +# ---- 报告 ---- + +@app.get("/api/reports") +def report_list(): + with db() as conn: + rows = conn.execute( + "SELECT * FROM reports ORDER BY report_date DESC" + ).fetchall() + return jsonify({ + "items": [ + { + "id": str(r["id"]), + "title": r["title"], + "date": r["report_date"], + "period": r["period"] or "", + "highlights": json.loads(r["highlights_json"]) if r["highlights_json"] else [], + "sections": json.loads(r["sections_json"]) if r["sections_json"] else [], + } + for r in rows + ], + }) + + +# ---- 报告生成 ---- + +@app.post("/api/reports/generate") +def report_generate(): + days = (request.get_json(silent=True) or {}).get("days", 7) + try: + proc = subprocess.Popen( + [sys.executable, str(BASE_DIR / "structurer.py"), "report", "--days", str(days)], + stdout=subprocess.PIPE, stderr=subprocess.PIPE, + cwd=str(BASE_DIR), + ) + stdout, stderr = proc.communicate(timeout=180) + if proc.returncode == 0: + output = stdout.decode("utf-8", errors="replace").strip() + try: + result = json.loads(output.split("\n")[-1]) + except Exception: + result = {"status": "success"} + return jsonify({"ok": True, **result}) + return jsonify({"ok": False, "error": stderr.decode("utf-8", errors="replace")[:500]}), 500 + except subprocess.TimeoutExpired: + return jsonify({"ok": False, "error": "生成超时"}), 504 + except Exception as exc: + return jsonify({"ok": False, "error": str(exc)}), 500 + + +# ---- 行业信号 ---- + +@app.get("/api/signals") +def signal_list(): + days = request.args.get("days", 30, type=int) + from datetime import datetime, timedelta + cutoff = (datetime.now() - timedelta(days=days)).strftime("%Y-%m-%d") + + with db() as conn: + rows = conn.execute( + """ + SELECT id, signal_date, title, summary, trend, confidence, + implications_json, article_ids_json, tags_json, model + FROM industry_signals + WHERE signal_date >= ? + ORDER BY signal_date DESC, confidence DESC + """, + (cutoff,), + ).fetchall() + + # 批量查关联文章标题 + all_ids = set() + for r in rows: + for aid in json.loads(r["article_ids_json"] or "[]"): + all_ids.add(aid) + article_map = {} + if all_ids: + placeholders = ",".join("?" * len(all_ids)) + for ar in conn.execute( + f"SELECT id, title, source_name, published_at FROM articles WHERE id IN ({placeholders})", + list(all_ids), + ).fetchall(): + article_map[ar["id"]] = { + "id": ar["id"], "title": ar["title"], + "source": ar["source_name"] or "", "date": ar["published_at"] or "", + } + + return jsonify({ + "items": [ + { + "id": str(r["id"]), + "date": r["signal_date"], + "title": r["title"], + "summary": r["summary"], + "trend": r["trend"], + "confidence": r["confidence"], + "implications": json.loads(r["implications_json"] or "[]"), + "articles": [ + article_map[aid] for aid in json.loads(r["article_ids_json"] or "[]") + if aid in article_map + ], + "tags": json.loads(r["tags_json"] or "[]"), + "model": r["model"], + } + for r in rows + ], + }) + + +# ---- RAG 问答 ---- + +@app.post("/api/qa/ask") +def qa_ask(): + data = request.get_json(silent=True) or {} + question = (data.get("question") or "").strip() + if not question: + return jsonify({"ok": False, "error": "请输入问题"}), 400 + + try: + from rag import rag_qa + result = rag_qa(question) + return jsonify({"ok": True, **result}) + except Exception as exc: + return jsonify({"ok": False, "error": str(exc)}), 502 + + +@app.post("/api/qa/stream") +def qa_stream(): + """SSE 流式问答端点。""" + data = request.get_json(silent=True) or {} + question = (data.get("question") or "").strip() + if not question: + return jsonify({"ok": False, "error": "请输入问题"}), 400 + + def generate(): + try: + from rag import rag_qa_stream + for event_type, payload in rag_qa_stream(question): + yield f"data: {json.dumps({'type': event_type, 'data': payload}, ensure_ascii=False)}\n\n" + yield "data: [DONE]\n\n" + except Exception as exc: + yield f"data: {json.dumps({'type': 'error', 'data': str(exc)}, ensure_ascii=False)}\n\n" + yield "data: [DONE]\n\n" + + return Response( + stream_with_context(generate()), + mimetype="text/event-stream", + headers={ + "Cache-Control": "no-cache", + "X-Accel-Buffering": "no", + "Connection": "keep-alive", + }, + ) + + +# ---- 触发采集 ---- + +@app.post("/api/crawl/run") +def crawl_run(): + data = request.get_json(silent=True) or {} + source_type = data.get("source_type", "公众号") + try: + proc = subprocess.Popen( + [sys.executable, str(BASE_DIR / "crawler.py"), "--source-type", source_type], + stdout=subprocess.PIPE, stderr=subprocess.PIPE, + cwd=str(BASE_DIR), + ) + return jsonify({"ok": True, "message": f"采集任务已启动({source_type}),PID={proc.pid}"}) + except Exception as exc: + return jsonify({"ok": False, "error": str(exc)}), 500 + + +# ---- 触发结构化分析 ---- + +@app.post("/api/analyze/run") +def analyze_run(): + data = request.get_json(silent=True) or {} + force = bool(data.get("force", False)) + try: + cmd = [sys.executable, str(BASE_DIR / "structurer.py"), "analyze"] + if force: + cmd.append("--force") + proc = subprocess.Popen( + cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, + cwd=str(BASE_DIR), + ) + return jsonify({"ok": True, "message": f"结构化分析任务已启动,PID={proc.pid}"}) + except Exception as exc: + return jsonify({"ok": False, "error": str(exc)}), 500 + + +# ---- 采集运行记录 ---- + +@app.get("/api/crawl/runs") +def crawl_runs(): + with db() as conn: + rows = conn.execute( + "SELECT * FROM crawl_runs ORDER BY started_at DESC LIMIT 20" + ).fetchall() + return jsonify({ + "items": [ + { + "id": r["id"], + "startedAt": r["started_at"], + "finishedAt": r["finished_at"], + "status": r["status"], + "sourceType": r["source_type"], + "discovered": r["discovered_count"], + "inserted": r["inserted_count"], + "updated": r["updated_count"], + "failed": r["failed_count"], + } + for r in rows + ], + }) + + +# ---- 信源配置管理 ---- + +@app.get("/api/sources") +def sources_list(): + source_type = request.args.get("type", "") + with db() as conn: + if source_type: + rows = conn.execute( + "SELECT * FROM sources WHERE source_type=? ORDER BY enabled DESC, id DESC", + (source_type,), + ).fetchall() + else: + rows = conn.execute( + "SELECT * FROM sources ORDER BY source_type, enabled DESC, id DESC" + ).fetchall() + return jsonify({ + "items": [ + { + "id": r["id"], + "sourceType": r["source_type"], + "name": r["name"], + "finder": r["finder"] or "", + "listUrl": r["list_url"] or "", + "downloadDir": r["download_dir"] or "", + "enabled": bool(r["enabled"]), + "lastCrawledAt": r["last_crawled_at"], + } + for r in rows + ], + }) + + +@app.post("/api/sources") +def source_create(): + data = request.get_json(silent=True) or {} + name = (data.get("name") or "").strip() + source_type = data.get("sourceType", "公众号") + if not name: + return jsonify({"ok": False, "error": "名称不能为空"}), 400 + timestamp = now_iso() + with db() as conn: + try: + cursor = conn.execute( + """ + INSERT INTO sources (source_type, name, finder, list_url, download_dir, enabled, created_at, updated_at) + VALUES (?, ?, ?, ?, ?, ?, ?, ?) + """, + ( + source_type, name, + data.get("finder", ""), data.get("listUrl", ""), data.get("downloadDir", ""), + int(data.get("enabled", True)), timestamp, timestamp, + ), + ) + conn.commit() + return jsonify({"ok": True, "id": cursor.lastrowid}) + except sqlite3.IntegrityError: + return jsonify({"ok": False, "error": f"{source_type}「{name}」已存在"}), 409 + + +@app.put("/api/sources/") +def source_update(source_id: int): + data = request.get_json(silent=True) or {} + timestamp = now_iso() + with db() as conn: + row = conn.execute("SELECT * FROM sources WHERE id=?", (source_id,)).fetchone() + if not row: + return jsonify({"ok": False, "error": "信源不存在"}), 404 + conn.execute( + """ + UPDATE sources SET + name=?, finder=?, list_url=?, download_dir=?, enabled=?, updated_at=? + WHERE id=? + """, + ( + (data.get("name") or row["name"]).strip(), + data.get("finder", row["finder"]), + data.get("listUrl", row["list_url"]), + data.get("downloadDir", row["download_dir"]), + int(data.get("enabled", bool(row["enabled"]))), + timestamp, source_id, + ), + ) + conn.commit() + return jsonify({"ok": True}) + + +@app.delete("/api/sources/") +def source_delete(source_id: int): + with db() as conn: + row = conn.execute("SELECT * FROM sources WHERE id=?", (source_id,)).fetchone() + if not row: + return jsonify({"ok": False, "error": "信源不存在"}), 404 + conn.execute("DELETE FROM sources WHERE id=?", (source_id,)) + conn.commit() + return jsonify({"ok": True}) + + +@app.post("/api/sources//toggle") +def source_toggle(source_id: int): + with db() as conn: + row = conn.execute("SELECT enabled FROM sources WHERE id=?", (source_id,)).fetchone() + if not row: + return jsonify({"ok": False, "error": "信源不存在"}), 404 + new_val = 0 if row["enabled"] else 1 + conn.execute("UPDATE sources SET enabled=?, updated_at=? WHERE id=?", (new_val, now_iso(), source_id)) + conn.commit() + return jsonify({"ok": True, "enabled": bool(new_val)}) + + +if __name__ == "__main__": + ensure_schema() + port = int(os.getenv("PORT", "8765")) + app.run(host="127.0.0.1", port=port, debug=True) diff --git a/backend/com.cibank.daily-pipeline.plist b/backend/com.cibank.daily-pipeline.plist new file mode 100644 index 0000000..03de3d4 --- /dev/null +++ b/backend/com.cibank.daily-pipeline.plist @@ -0,0 +1,31 @@ + + + + + Label + com.cibank.daily-pipeline + ProgramArguments + + /usr/local/bin/python3 + /Users/freedak/Documents/AIDashboard/CIBank/backend/daily_pipeline.py + daily + + WorkingDirectory + /Users/freedak/Documents/AIDashboard/CIBank/backend + StartCalendarInterval + + Hour + 2 + Minute + 0 + + ProcessType + Background + LowPriorityIO + + StandardOutPath + /tmp/cibank-daily-pipeline.log + StandardErrorPath + /tmp/cibank-daily-pipeline-error.log + + diff --git a/backend/crawler.py b/backend/crawler.py new file mode 100644 index 0000000..eac2149 --- /dev/null +++ b/backend/crawler.py @@ -0,0 +1,569 @@ +#!/usr/bin/env python3 +"""采集管道:公众号历史文章抓取 + 视频号 ASR 转写。 + +合规采集策略: +- 控制请求频率,每篇文章间隔 CRAWL_ARTICLE_PAUSE 秒(默认2秒) +- 列表页滚动间隔 CRAWL_LIST_PAUSE 秒(默认1.5秒) +- 最多滚动 CRAWL_MAX_SCROLLS 次(默认15次) +- 回溯天数 CRAWL_LOOKBACK_DAYS(默认7天),避免全量抓取 +- 支持 Playwright 真实浏览器渲染,避免被反爬 +- 视频号内容通过 ASR API 转写为文本后入库 +""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import os +import re +import sqlite3 +import subprocess +import sys +import time +import urllib.error +import urllib.request +from contextlib import closing +from datetime import datetime, timedelta, timezone +from pathlib import Path +from typing import Any, Iterable +from urllib.parse import urljoin, urlsplit, urlunsplit + +from dotenv import load_dotenv + +BASE_DIR = Path(__file__).resolve().parent +load_dotenv(BASE_DIR / ".env") + +DEFAULT_LIST_URL = "https://m.canyin88.com/zixun/" +ARTICLE_PATH_RE = re.compile(r"/zixun/\d{4}/\d{1,2}/\d{1,2}/\d+\.html$") +DATE_RE = re.compile(r"(20\d{2})[-/.年](\d{1,2})[-/.月](\d{1,2})(?:日)?(?:\s+(\d{1,2}):(\d{2})(?::(\d{2}))?)?") + + +def now_iso() -> str: + return datetime.now(timezone.utc).astimezone().isoformat(timespec="seconds") + + +def normalize_url(raw_url: str, base_url: str = DEFAULT_LIST_URL) -> str | None: + absolute = urljoin(base_url, raw_url.strip()) + parts = urlsplit(absolute) + host = parts.netloc.lower().removeprefix("m.").removeprefix("www.") + if host != "canyin88.com" or not ARTICLE_PATH_RE.search(parts.path): + return None + return urlunsplit(("https", "www.canyin88.com", parts.path, "", "")) + + +def clean_text(value: str | None) -> str: + return re.sub(r"\s+", " ", value or "").strip().lstrip("\ufeff") + + +def parse_date(value: str | None) -> str | None: + if not value: + return None + match = DATE_RE.search(value) + if not match: + return None + year, month, day, hour, minute, second = match.groups() + dt = datetime( + int(year), int(month), int(day), int(hour or 0), int(minute or 0), int(second or 0) + ) + return dt.isoformat(timespec="seconds") + + +def date_from_article_url(url: str) -> datetime | None: + match = re.search(r"/zixun/(20\d{2})/(\d{1,2})/(\d{1,2})/", url) + if not match: + return None + return datetime(*(int(part) for part in match.groups())) + + +def init_db(conn: sqlite3.Connection, schema_path: Path) -> None: + conn.executescript(schema_path.read_text(encoding="utf-8")) + + +# ---- 频率控制器 ---- + +class RateLimiter: + """简单的频率控制器,确保请求间隔不低于指定秒数。""" + + def __init__(self, min_interval: float): + self.min_interval = min_interval + self._last_time = 0.0 + + def wait(self) -> None: + elapsed = time.time() - self._last_time + if elapsed < self.min_interval: + time.sleep(self.min_interval - elapsed) + self._last_time = time.time() + + +# ---- 公众号文章抓取 ---- + +def discover_urls(page, list_url: str, scrolls: int, pause_ms: int) -> list[str]: + """在列表页滚动发现文章URL,控制滚动频率。""" + page.goto(list_url, wait_until="domcontentloaded", timeout=60_000) + page.wait_for_timeout(2_000) + found: set[str] = set() + stagnant = 0 + for _ in range(scrolls + 1): + hrefs = page.locator("a[href]").evaluate_all("els => els.map(e => e.href)") + before = len(found) + for href in hrefs: + normalized = normalize_url(href, list_url) + if normalized: + found.add(normalized) + stagnant = stagnant + 1 if len(found) == before else 0 + if stagnant >= 3: + break + page.evaluate("window.scrollTo(0, document.body.scrollHeight)") + page.wait_for_timeout(pause_ms) + return sorted(found, reverse=True) + + +def meta(page, selector: str) -> str: + locator = page.locator(selector).first + return clean_text(locator.get_attribute("content")) if locator.count() else "" + + +def first_text(page, selectors: list[str]) -> str: + for selector in selectors: + locator = page.locator(selector).first + if locator.count(): + text = clean_text(locator.inner_text(timeout=3_000)) + if text: + return text + return "" + + +def extract_article(page, url: str) -> dict[str, str | None]: + page.goto(url, wait_until="domcontentloaded", timeout=60_000) + page.wait_for_timeout(800) + document_title = clean_text(page.title()) + document_title = re.sub(r"(?:[_-]红餐网.*)$", "", document_title).strip() + title = meta(page, 'meta[property="og:title"]') or document_title + if not title or title == "相关推荐": + title = first_text(page, [".title", "h1"]) + summary = ( + meta(page, 'meta[name="description"]') + or meta(page, 'meta[property="og:description"]') + ) + author = ( + meta(page, 'meta[name="author"]') + or first_text(page, [".author", ".source", "[class*=author]"]) + ) + published_raw = ( + meta(page, 'meta[property="article:published_time"]') + or first_text(page, ["time", ".time", "[class*=time]", "[class*=date]"]) + or page.locator("body").inner_text(timeout=5_000)[:1000] + ) + content = first_text( + page, + [ + "article", + ".article-content", + ".article_content", + ".content", + "[class*=detail-content]", + "[class*=article] [class*=content]", + ], + ) + if not content: + paragraphs = page.locator("p").all_inner_texts() + content = "\n\n".join(clean_text(p) for p in paragraphs if len(clean_text(p)) >= 20) + if not title or len(content) < 80: + raise ValueError(f"article extraction incomplete: title={bool(title)}, content_length={len(content)}") + published_at = parse_date(published_raw) + url_date = date_from_article_url(url) + if url_date and ( + not published_at or datetime.fromisoformat(published_at).date() != url_date.date() + ): + published_at = url_date.isoformat(timespec="seconds") + content_hash = hashlib.sha256(f"{title}\n{content}".encode("utf-8")).hexdigest() + return { + "canonical_url": url, + "title": title, + "author": author or None, + "published_at": published_at, + "summary": summary or None, + "content": content, + "content_hash": content_hash, + } + + +def pending_urls(conn: sqlite3.Connection, discovered: list[str], max_retries: int) -> list[str]: + rows = conn.execute( + "SELECT canonical_url FROM articles WHERE crawl_status != 'success' AND retry_count < ?", + (max_retries,), + ).fetchall() + return list(dict.fromkeys(discovered + [row[0] for row in rows])) + + +def insert_discoveries(conn: sqlite3.Connection, urls: list[str], source_type: str = "公众号") -> int: + timestamp = now_iso() + inserted = 0 + for url in urls: + cursor = conn.execute( + """ + INSERT OR IGNORE INTO articles + (canonical_url, source_type, discovered_at, updated_at) + VALUES (?, ?, ?, ?) + """, + (url, source_type, timestamp, timestamp), + ) + inserted += cursor.rowcount + conn.commit() + return inserted + + +def save_success(conn: sqlite3.Connection, article: dict[str, str | None]) -> None: + timestamp = now_iso() + conn.execute( + """ + UPDATE articles SET + title = ?, author = ?, published_at = ?, summary = ?, content = ?, + content_hash = ?, crawl_status = 'success', last_error = NULL, + crawled_at = ?, updated_at = ? + WHERE canonical_url = ? + """, + ( + article["title"], article["author"], article["published_at"], article["summary"], + article["content"], article["content_hash"], timestamp, timestamp, + article["canonical_url"], + ), + ) + conn.commit() + + +def save_failure(conn: sqlite3.Connection, url: str, error: Exception) -> None: + conn.execute( + """ + UPDATE articles SET + crawl_status = 'failed', retry_count = retry_count + 1, + last_error = ?, updated_at = ? + WHERE canonical_url = ? + """, + (str(error)[:1000], now_iso(), url), + ) + conn.commit() + + +# ---- 视频号本地处理(参考 VIBank 方案) ---- + +VIDEO_SUFFIXES = {".mp4", ".mov", ".m4v", ".mkv", ".webm"} +FFMPEG = os.getenv("FFMPEG_PATH", "/usr/local/bin/ffmpeg") +FFPROBE = os.getenv("FFPROBE_PATH", "/usr/local/bin/ffprobe") +FUNASR = os.getenv("FUNASR_PATH", "/usr/local/bin/funasr") +ASR_MODEL = os.getenv("ASR_MODEL", "sensevoice") +ASR_HOTWORDS = os.getenv("ASR_HOTWORDS", "餐饮,餐饮连锁,连锁餐厅,单店模型,供应链,选址,复购,翻台率,客单价,毛利率,净利率,加盟,直营") + + +def load_video_config() -> dict[str, Any] | None: + """加载视频源配置:优先从数据库 sources 表读取,降级到 JSON 文件。""" + # 优先从数据库读取 + db_path = BASE_DIR / "data" / "cibank.db" + if db_path.exists(): + try: + with sqlite3.connect(db_path) as conn: + rows = conn.execute( + "SELECT name, finder, download_dir FROM sources WHERE source_type='视频号' AND enabled=1" + ).fetchall() + if rows: + return { + "sources": [ + {"account": r[0], "finder": r[1] or "", "directory": r[2] or ""} + for r in rows + ] + } + except sqlite3.Error: + pass + + # 降级到 JSON 配置文件 + config_path = BASE_DIR / "video_config.json" + if config_path.exists(): + return json.loads(config_path.read_text(encoding="utf-8")) + legacy = BASE_DIR / "video_sources.json" + if legacy.exists(): + return {"sources": [], "legacy_urls": json.loads(legacy.read_text(encoding="utf-8")).get("videos", [])} + return None + + +def file_fingerprint(path: Path) -> str: + stat = path.stat() + h = hashlib.sha256() + h.update(f"{stat.st_size}:{stat.st_mtime_ns}".encode()) + with path.open("rb") as f: + h.update(f.read(1024 * 1024)) + if stat.st_size > 2 * 1024 * 1024: + f.seek(max(0, stat.st_size - 1024 * 1024)) + h.update(f.read(1024 * 1024)) + return h.hexdigest() + + +def safe_stem(name: str) -> str: + value = re.sub(r"[_ ]?xWT\d+$", "", Path(name).stem, flags=re.I) + value = re.sub(r"[\x00-\x1f/:]", "_", value).strip(" .") + return value[:180] or "untitled" + + +def ffprobe_duration(path: Path) -> float: + result = subprocess.run( + [FFPROBE, "-v", "error", "-show_entries", "format=duration", + "-of", "default=noprint_wrappers=1:nokey=1", str(path)], + text=True, capture_output=True, check=True, timeout=30, + ) + return float(result.stdout.strip()) + + +def extract_audio(video: Path, audio_dir: Path) -> Path: + """用 FFmpeg 从视频中提取 16kHz 单声道 WAV。""" + audio_dir.mkdir(parents=True, exist_ok=True) + fingerprint = file_fingerprint(video)[:10] + wav = audio_dir / f"{safe_stem(video.name)}_{fingerprint}.wav" + if wav.is_file() and wav.stat().st_size > 44: + return wav + partial = wav.with_name(wav.name + ".part") + partial.unlink(missing_ok=True) + try: + subprocess.run( + [FFMPEG, "-hide_banner", "-loglevel", "error", "-y", + "-i", str(video), "-vn", "-ac", "1", "-ar", "16000", "-c:a", "pcm_s16le", str(partial)], + check=True, timeout=1800, + ) + partial.replace(wav) + return wav + finally: + partial.unlink(missing_ok=True) + + +def funasr_transcribe(wav_path: Path) -> str: + """用 FunASR/SenseVoice 进行本地中文转写。""" + cmd = [FUNASR, str(wav_path), "--model", ASR_MODEL, "--language", "zh", "--output-format", "json"] + if ASR_HOTWORDS: + cmd.extend(["--hotwords", ASR_HOTWORDS]) + result = subprocess.run(cmd, text=True, capture_output=True, check=True, timeout=3600) + start = result.stdout.find("{") + if start < 0: + raise RuntimeError("FunASR 未返回 JSON") + payload = json.loads(result.stdout[start:]) + text = re.sub(r"<\|[^>]+\|>", "", str(payload.get("text", ""))).strip() + if not text: + raise RuntimeError("FunASR 返回空文本") + return text + + +def discover_local_videos(config: dict[str, Any]) -> Iterable[tuple[dict[str, str], Path]]: + """扫描配置中的视频目录,发现本地视频文件。""" + for source in config.get("sources", []): + root = Path(source["directory"]) + if not root.is_dir(): + print(f"WARN 视频源目录不存在: {root}", file=sys.stderr) + continue + for path in sorted(root.iterdir()): + if path.is_file() and path.suffix.lower() in VIDEO_SUFFIXES and not path.name.startswith("."): + yield source, path + + +def process_local_video(source: dict[str, str], video: Path, audio_root: Path) -> dict[str, str | None | float]: + """完整处理一个本地视频:提取音频 → ASR 转写 → 返回结构化数据。""" + duration = ffprobe_duration(video) + account = source.get("account", "未知视频号") + audio_dir = audio_root / re.sub(r"[/:]", "_", account) + wav = extract_audio(video, audio_dir) + transcript = funasr_transcribe(wav) + title = safe_stem(video.name) + content_hash = hashlib.sha256(f"{title}\n{transcript}".encode("utf-8")).hexdigest() + media_url = str(video.resolve()) + media_key = hashlib.sha256(media_url.encode("utf-8")).hexdigest()[:24] + return { + "canonical_url": f"video://local/{media_key}", + "media_url": media_url, + "title": title, + "content": transcript, + "summary": transcript[:200] if transcript else "", + "author": account, + "published_at": now_iso(), + "content_hash": content_hash, + "duration": duration, + } + + +# ---- 主采集流程 ---- + +def run_crawl(args: argparse.Namespace) -> int: + db_path = Path(args.db).expanduser().resolve() + db_path.parent.mkdir(parents=True, exist_ok=True) + + article_pause = float(os.getenv("CRAWL_ARTICLE_PAUSE", "2.0")) + list_pause_ms = int(float(os.getenv("CRAWL_LIST_PAUSE", "1.5")) * 1000) + max_scrolls = int(os.getenv("CRAWL_MAX_SCROLLS", "15")) + lookback_days = int(os.getenv("CRAWL_LOOKBACK_DAYS", "7")) + + article_limiter = RateLimiter(article_pause) + + with closing(sqlite3.connect(db_path)) as conn: + init_db(conn, BASE_DIR / "schema.sql") + started_at = now_iso() + run_id = conn.execute( + "INSERT INTO crawl_runs(started_at, source_type) VALUES (?, ?)", + (started_at, args.source_type), + ).lastrowid + conn.commit() + + discovered_count = inserted_count = updated_count = failed_count = 0 + try: + if args.source_type == "公众号": + # 从数据库读取启用的公众号信源,降级到命令行参数 + mp_sources = conn.execute( + "SELECT name, list_url FROM sources WHERE source_type='公众号' AND enabled=1 AND list_url!=''" + ).fetchall() + if mp_sources: + list_urls = [(r[0], r[1]) for r in mp_sources] + else: + list_urls = [("默认", args.list_url)] + + from playwright.sync_api import sync_playwright + with sync_playwright() as playwright: + browser = playwright.chromium.launch(headless=not args.headed) + context = browser.new_context( + locale="zh-CN", + timezone_id="Asia/Shanghai", + viewport={"width": 1280, "height": 900}, + user_agent=( + "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) " + "AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0 Safari/537.36" + ), + ) + context.set_default_timeout(15_000) + page = context.new_page() + try: + for source_name, list_url in list_urls: + print(f"--- 采集公众号: {source_name} ({list_url}) ---", flush=True) + urls = discover_urls(page, list_url, max_scrolls, list_pause_ms) + discovered_count += len(urls) + inserted_count += insert_discoveries(conn, urls, source_name) + queue = pending_urls(conn, urls, args.max_retries) + cutoff = datetime.now() - timedelta(days=lookback_days) + for index, url in enumerate(queue, start=1): + path_date = parse_date(url.replace("/", "-")) + if path_date and datetime.fromisoformat(path_date) < cutoff: + continue + article_limiter.wait() + try: + article = extract_article(page, url) + save_success(conn, article) + updated_count += 1 + print(f"[{source_name} {index}/{len(queue)}] OK {article['title']}", flush=True) + except Exception as exc: + failed_count += 1 + save_failure(conn, url, exc) + print(f"[{source_name} {index}/{len(queue)}] FAIL {url}: {exc}", file=sys.stderr, flush=True) + # 更新信源的最近采集时间 + conn.execute( + "UPDATE sources SET last_crawled_at=? WHERE source_type='公众号' AND name=?", + (now_iso(), source_name), + ) + conn.commit() + finally: + context.close() + elif args.source_type == '视频号': + # 视频号本地处理:扫描本地视频目录 → FFmpeg 提取音频 → FunASR 转写 + video_config = load_video_config() + if not video_config: + print(json.dumps({"status": "skipped", "reason": "未找到 video_config.json 或 video_sources.json"}, ensure_ascii=False)) + conn.execute( + "UPDATE crawl_runs SET finished_at=?, status='skipped', message=? WHERE id=?", + (now_iso(), "未配置视频源", run_id), + ) + conn.commit() + return 0 + + audio_root = Path(os.getenv("VIDEO_AUDIO_ROOT", str(BASE_DIR / "data" / "audio"))) + video_files = list(discover_local_videos(video_config)) + discovered_count = len(video_files) + + for index, (source, video) in enumerate(video_files, start=1): + media_url = str(video.resolve()) + existing_media = conn.execute( + "SELECT id FROM articles WHERE media_url=?", (media_url,) + ).fetchone() + if existing_media: + print(f"[{index}/{discovered_count}] SKIP {video.stem}(已入库)", flush=True) + continue + article_limiter.wait() + try: + result = process_local_video(source, video, audio_root) + timestamp = now_iso() + existing = conn.execute( + "SELECT id, content_hash FROM articles WHERE canonical_url=?", + (result["canonical_url"],), + ).fetchone() + if existing and existing[1] == result["content_hash"]: + print(f"[{index}/{discovered_count}] SKIP {result['title']}(已入库)", flush=True) + continue + conn.execute( + """ + INSERT INTO articles + (canonical_url, source, source_type, source_name, title, author, + published_at, summary, content, content_hash, media_url, + crawl_status, discovered_at, crawled_at, updated_at) + VALUES (?, 'video_local', '视频号', ?, ?, ?, ?, ?, ?, ?, ?, 'success', ?, ?, ?) + ON CONFLICT(canonical_url) DO UPDATE SET + title=excluded.title, summary=excluded.summary, content=excluded.content, + content_hash=excluded.content_hash, media_url=excluded.media_url, + crawled_at=excluded.crawled_at, updated_at=excluded.updated_at, + crawl_status='success', last_error=NULL + """, + ( + result["canonical_url"], source.get("account", ""), + result["title"], result["author"], result["published_at"], + result["summary"], result["content"], result["content_hash"], + result["media_url"], timestamp, timestamp, timestamp, + ), + ) + conn.commit() + updated_count += 1 + print(f"[{index}/{discovered_count}] OK {result['title']}({result.get('duration', 0):.0f}s)", flush=True) + # 更新信源最近采集时间 + conn.execute( + "UPDATE sources SET last_crawled_at=? WHERE source_type='视频号' AND name=?", + (now_iso(), source.get("account", "")), + ) + conn.commit() + except Exception as exc: + failed_count += 1 + print(f"[{index}/{discovered_count}] FAIL {video.name}: {exc}", file=sys.stderr, flush=True) + + status = "success" if failed_count == 0 else "partial" + message = json.dumps({"db": str(db_path)}, ensure_ascii=False) + except Exception as exc: + status, message = "failed", str(exc)[:1000] + print(f"crawl failed: {exc}", file=sys.stderr) + + conn.execute( + """ + UPDATE crawl_runs SET finished_at=?, status=?, discovered_count=?, + inserted_count=?, updated_count=?, failed_count=?, message=? + WHERE id=? + """, + (now_iso(), status, discovered_count, inserted_count, updated_count, failed_count, message, run_id), + ) + conn.commit() + print(json.dumps({ + "status": status, "source_type": args.source_type, + "discovered": discovered_count, "inserted": inserted_count, + "updated": updated_count, "failed": failed_count, + }, ensure_ascii=False, flush=True)) + return 0 if status in {"success", "partial"} else 1 + + +def build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser(description="餐饮内容采集管道") + parser.add_argument("--db", default=str(BASE_DIR / "data" / "cibank.db"), help="SQLite 数据库路径") + parser.add_argument("--list-url", default=DEFAULT_LIST_URL, help="公众号列表页URL") + parser.add_argument("--source-type", choices=["公众号", "视频号"], default="公众号", help="采集来源类型") + parser.add_argument("--max-retries", type=int, default=3) + parser.add_argument("--headed", action="store_true", help="显示浏览器(调试用)") + return parser + + +if __name__ == "__main__": + raise SystemExit(run_crawl(build_parser().parse_args())) diff --git a/backend/daily_pipeline.py b/backend/daily_pipeline.py new file mode 100644 index 0000000..3fce0ef --- /dev/null +++ b/backend/daily_pipeline.py @@ -0,0 +1,198 @@ +#!/usr/bin/env python3 +from __future__ import annotations + +import argparse +import fcntl +import json +import os +import sqlite3 +import subprocess +import sys +import time +from datetime import datetime +from pathlib import Path + +from dotenv import load_dotenv + +BASE_DIR = Path(__file__).resolve().parent +load_dotenv(BASE_DIR / ".env") +DATA_DIR = BASE_DIR / "data" +DB_PATH = Path(os.getenv("CIBANK_DB", DATA_DIR / "cibank.db")) +LOCK_PATH = DATA_DIR / "pipeline.lock" +LOG_DIR = DATA_DIR / "logs" +BACKUP_DIR = DATA_DIR / "backups" +SCHEMA_PATH = BASE_DIR / "schema.sql" + + +def now_iso() -> str: + return datetime.now().astimezone().isoformat(timespec="seconds") + + +def open_db() -> sqlite3.Connection: + conn = sqlite3.connect(DB_PATH) + conn.row_factory = sqlite3.Row + conn.executescript(SCHEMA_PATH.read_text(encoding="utf-8")) + return conn + + +def acquire_lock(): + DATA_DIR.mkdir(parents=True, exist_ok=True) + lock_file = LOCK_PATH.open("a+") + try: + fcntl.flock(lock_file.fileno(), fcntl.LOCK_EX | fcntl.LOCK_NB) + except BlockingIOError as exc: + lock_file.close() + raise RuntimeError("已有流水线任务正在运行") from exc + lock_file.seek(0) + lock_file.truncate() + lock_file.write(f"{os.getpid()}\n{now_iso()}\n") + lock_file.flush() + return lock_file + + +def backup_database() -> str: + BACKUP_DIR.mkdir(parents=True, exist_ok=True) + backup_path = BACKUP_DIR / f"cibank-{datetime.now().strftime('%Y%m%d-%H%M%S')}.db" + source = sqlite3.connect(DB_PATH) + target = sqlite3.connect(backup_path) + try: + source.backup(target) + finally: + target.close() + source.close() + backups = sorted(BACKUP_DIR.glob("cibank-*.db"), key=lambda path: path.stat().st_mtime, reverse=True) + for expired in backups[14:]: + expired.unlink() + return str(backup_path) + + +def create_run(run_type: str) -> int: + timestamp = now_iso() + with open_db() as conn: + cursor = conn.execute( + """ + INSERT INTO pipeline_runs + (run_type, started_at, status, current_step, steps_json, created_at) + VALUES (?, ?, 'running', 'preflight', '{}', ?) + """, + (run_type, timestamp, timestamp), + ) + conn.commit() + return cursor.lastrowid + + +def update_run(run_id: int, **values) -> None: + allowed = {"finished_at", "status", "current_step", "steps_json", "error_message"} + fields = [(key, value) for key, value in values.items() if key in allowed] + if not fields: + return + assignments = ", ".join(f"{key}=?" for key, _ in fields) + with open_db() as conn: + conn.execute( + f"UPDATE pipeline_runs SET {assignments} WHERE id=?", + [value for _, value in fields] + [run_id], + ) + conn.commit() + + +def run_step(run_id: int, name: str, arguments: list[str], log_file, steps: dict) -> bool: + update_run(run_id, current_step=name) + started = time.monotonic() + log_file.write(f"\n[{now_iso()}] START {name}: {' '.join(arguments)}\n") + log_file.flush() + env = os.environ.copy() + env["PATH"] = "/opt/homebrew/bin:/usr/local/bin:/usr/bin:/bin:" + env.get("PATH", "") + result = subprocess.run( + [sys.executable, *arguments], + cwd=BASE_DIR, + env=env, + stdout=log_file, + stderr=subprocess.STDOUT, + text=True, + check=False, + ) + duration = round(time.monotonic() - started, 2) + steps[name] = {"status": "success" if result.returncode == 0 else "failed", "returncode": result.returncode, "duration": duration} + update_run(run_id, steps_json=json.dumps(steps, ensure_ascii=False)) + log_file.write(f"[{now_iso()}] END {name}: code={result.returncode}, duration={duration}s\n") + log_file.flush() + return result.returncode == 0 + + +def validate_environment() -> None: + if not DB_PATH.exists(): + raise RuntimeError(f"数据库不存在:{DB_PATH}") + api_key = os.getenv("DASHSCOPE_API_KEY", "").strip() + if not api_key or api_key == "replace_with_your_key": + raise RuntimeError("DASHSCOPE_API_KEY 未配置") + with sqlite3.connect(DB_PATH) as conn: + result = conn.execute("PRAGMA integrity_check").fetchone()[0] + if result != "ok": + raise RuntimeError(f"数据库完整性检查失败:{result}") + + +def show_status() -> int: + with open_db() as conn: + row = conn.execute("SELECT * FROM pipeline_runs ORDER BY id DESC LIMIT 1").fetchone() + print(json.dumps(dict(row) if row else {"status": "never_run"}, ensure_ascii=False, indent=2)) + return 0 + + +def run_pipeline(run_type: str, skip_crawl: bool) -> int: + lock_file = acquire_lock() + run_id = None + LOG_DIR.mkdir(parents=True, exist_ok=True) + log_path = LOG_DIR / f"pipeline-{datetime.now().strftime('%Y%m%d-%H%M%S')}.log" + try: + validate_environment() + run_id = create_run(run_type) + steps = {"backup": {"status": "success", "path": backup_database()}} + update_run(run_id, steps_json=json.dumps(steps, ensure_ascii=False)) + commands = [] + if not skip_crawl: + commands.extend([ + ("crawl_mp", ["crawler.py", "--source-type", "公众号"]), + ("crawl_video", ["crawler.py", "--source-type", "视频号"]), + ]) + analyze_args = ["structurer.py", "analyze", "--pause", "0.6", "--retries", "3"] + embed_args = ["structurer.py", "embed"] + if run_type == "full": + analyze_args.append("--force") + embed_args.append("--force") + commands.extend([ + ("analyze", analyze_args), + ("embed", embed_args), + ("signals", ["structurer.py", "signals", "--days", "30"]), + ]) + all_success = True + with log_path.open("a", encoding="utf-8") as log_file: + log_file.write(f"[{now_iso()}] pipeline run_id={run_id}, type={run_type}\n") + for name, arguments in commands: + if not run_step(run_id, name, arguments, log_file, steps): + all_success = False + status = "success" if all_success else "partial" + update_run(run_id, finished_at=now_iso(), status=status, current_step="completed", steps_json=json.dumps(steps, ensure_ascii=False)) + print(json.dumps({"run_id": run_id, "status": status, "log": str(log_path), "steps": steps}, ensure_ascii=False, indent=2)) + return 0 if all_success else 1 + except Exception as exc: + if run_id is not None: + update_run(run_id, finished_at=now_iso(), status="failed", current_step="failed", error_message=str(exc)) + print(json.dumps({"status": "failed", "error": str(exc), "log": str(log_path)}, ensure_ascii=False), file=sys.stderr) + return 1 + finally: + fcntl.flock(lock_file.fileno(), fcntl.LOCK_UN) + lock_file.close() + + +def main() -> int: + parser = argparse.ArgumentParser(description="CIBank 自动增量处理流水线") + parser.add_argument("command", choices=["daily", "full", "status"]) + parser.add_argument("--skip-crawl", action="store_true") + args = parser.parse_args() + if args.command == "status": + return show_status() + return run_pipeline(args.command, args.skip_crawl) + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/backend/rag.py b/backend/rag.py new file mode 100644 index 0000000..d82a4a6 --- /dev/null +++ b/backend/rag.py @@ -0,0 +1,333 @@ +#!/usr/bin/env python3 +"""RAG 问答模块:向量检索 + LLM 生成。 + +流程: +1. 将用户问题转为向量 +2. 在 embeddings 表中做余弦相似度检索,取 TOP-K 相关文章 +3. 将检索到的文章内容作为上下文,调用千问生成回答 +4. 返回回答 + 引用来源(可追溯) +""" + +from __future__ import annotations + +import json +import os +import sqlite3 +import urllib.error +import urllib.request +from pathlib import Path + +import numpy as np + +from dotenv import load_dotenv +from structurer import get_api_key, get_embeddings, DASHSCOPE_CHAT_URL + +BASE_DIR = Path(__file__).resolve().parent +load_dotenv(BASE_DIR / ".env") + + +def cosine_similarity(a: np.ndarray, b: np.ndarray) -> float: + """计算两个向量的余弦相似度。""" + norm_a = np.linalg.norm(a) + norm_b = np.linalg.norm(b) + if norm_a == 0 or norm_b == 0: + return 0.0 + return float(np.dot(a, b) / (norm_a * norm_b)) + + +def vector_search(conn: sqlite3.Connection, query_embedding: list[float], top_k: int = 5) -> list[dict]: + """向量检索:在 embeddings 表中找到与查询向量最相似的文章。""" + query_vec = np.array(query_embedding, dtype=np.float32) + rows = conn.execute( + """ + SELECT e.article_id, e.embedding, e.chunk_text, + a.title, a.source_name, a.source_type, a.published_at, a.summary, + x.result_json + FROM embeddings e + JOIN articles a ON a.id = e.article_id + LEFT JOIN ai_analyses x ON x.id = ( + SELECT x2.id FROM ai_analyses x2 + WHERE x2.article_id = e.article_id AND x2.analysis_type = 'editorial' + ORDER BY x2.updated_at DESC, x2.id DESC LIMIT 1 + ) + WHERE a.crawl_status = 'success' + """ + ).fetchall() + + scored = [] + for row in rows: + emb = json.loads(row["embedding"]) + emb_vec = np.array(emb, dtype=np.float32) + score = cosine_similarity(query_vec, emb_vec) + scored.append((score, row)) + scored.sort(key=lambda x: x[0], reverse=True) + + results = [] + seen_articles = set() + for score, row in scored: + if row["article_id"] in seen_articles: + continue + seen_articles.add(row["article_id"]) + analysis = json.loads(row["result_json"]) if row["result_json"] else {} + results.append({ + "article_id": row["article_id"], + "title": row["title"], + "source": row["source_name"] or row["source_type"], + "date": (row["published_at"] or "")[:10], + "summary": analysis.get("summary") or row["summary"] or "", + "key_points": analysis.get("key_points", []), + "content": row["chunk_text"] or "", + "score": score, + }) + if len(results) >= top_k: + break + return results + + +def generate_answer(question: str, contexts: list[dict]) -> dict: + """基于检索到的上下文,调用千问生成回答。""" + api_key = get_api_key() + model = os.getenv("QWEN_MODEL", "qwen-plus") + + # 构建上下文 + context_text = "" + sources = [] + for i, ctx in enumerate(contexts, start=1): + context_text += f"\n--- 来源{i} ---\n标题:{ctx['title']}\n摘要:{ctx['summary']}\n要点:{'; '.join(ctx['key_points'][:3])}\n原文片段:{ctx['content']}\n" + sources.append({ + "title": ctx["title"], + "source": ctx["source"], + "date": ctx["date"], + }) + + prompt = f"""你是餐饮行业知识库的问答助手。根据以下知识库中的情报内容回答用户问题。 + +要求: +1. 回答必须基于提供的来源内容,不要编造信息 +2. 如果来源内容不足以回答问题,明确说明"知识库中暂无直接相关信息" +3. 回答要结构化、简洁、有经营参考价值 +4. 用中文回答 + +知识库来源: +{context_text} + +用户问题:{question}""" + + payload = json.dumps({ + "model": model, + "messages": [ + {"role": "system", "content": "你是餐饮行业知识库问答助手,基于库内情报给出有出处的专业回答。"}, + {"role": "user", "content": prompt}, + ], + "temperature": 0.3, + }, ensure_ascii=False).encode("utf-8") + + req = urllib.request.Request( + DASHSCOPE_CHAT_URL, + data=payload, + headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}, + method="POST", + ) + try: + with urllib.request.urlopen(req, timeout=90) as response: + body = json.loads(response.read().decode("utf-8")) + except urllib.error.HTTPError as exc: + detail = exc.read().decode("utf-8", errors="replace")[:500] + raise RuntimeError(f"千问接口返回 {exc.code}:{detail}") from exc + + answer = body["choices"][0]["message"]["content"].strip() + return { + "answer": answer, + "sources": sources, + "contexts": contexts, + } + + +def generate_answer_stream(question: str, contexts: list[dict]): + """流式生成回答,逐 token yield。""" + api_key = get_api_key() + model = os.getenv("QWEN_MODEL", "qwen-plus") + + context_text = "" + sources = [] + for i, ctx in enumerate(contexts, start=1): + context_text += f"\n--- 来源{i} ---\n标题:{ctx['title']}\n摘要:{ctx['summary']}\n要点:{'; '.join(ctx['key_points'][:3])}\n原文片段:{ctx['content']}\n" + sources.append({ + "title": ctx["title"], + "source": ctx["source"], + "date": ctx["date"], + }) + + prompt = f"""你是餐饮行业知识库的问答助手。根据以下知识库中的情报内容回答用户问题。 + +要求: +1. 回答必须基于提供的来源内容,不要编造信息 +2. 如果来源内容不足以回答问题,明确说明"知识库中暂无直接相关信息" +3. 回答要结构化、简洁、有经营参考价值 +4. 用中文回答,使用 Markdown 格式 + +知识库来源: +{context_text} + +用户问题:{question}""" + + payload = json.dumps({ + "model": model, + "messages": [ + {"role": "system", "content": "你是餐饮行业知识库问答助手,基于库内情报给出有出处的专业回答。使用 Markdown 格式输出。"}, + {"role": "user", "content": prompt}, + ], + "temperature": 0.3, + "stream": True, + "stream_options": {"include_usage": False}, + }, ensure_ascii=False).encode("utf-8") + + req = urllib.request.Request( + DASHSCOPE_CHAT_URL, + data=payload, + headers={ + "Authorization": f"Bearer {api_key}", + "Content-Type": "application/json", + "Accept": "text/event-stream", + }, + method="POST", + ) + try: + resp = urllib.request.urlopen(req, timeout=120) + except urllib.error.HTTPError as exc: + detail = exc.read().decode("utf-8", errors="replace")[:500] + raise RuntimeError(f"千问接口返回 {exc.code}:{detail}") from exc + + buffer = b"" + while True: + chunk = resp.read(4096) + if not chunk: + break + buffer += chunk + while b"\n" in buffer: + line, buffer = buffer.split(b"\n", 1) + line = line.strip() + if not line or line == b"data: [DONE]": + continue + if line.startswith(b"data:"): + try: + data = json.loads(line[5:].strip()) + delta = data.get("choices", [{}])[0].get("delta", {}) + content = delta.get("content") + if content: + yield content + except (json.JSONDecodeError, IndexError, KeyError): + pass + resp.close() + + yield json.dumps({"__sources__": sources}, ensure_ascii=False) + + +def rag_qa(question: str, top_k: int = 5) -> dict: + """完整的 RAG 问答流程:向量检索 → LLM 生成。""" + db_path = Path(os.getenv("CIBANK_DB", str(BASE_DIR / "data" / "cibank.db"))) + conn = sqlite3.connect(db_path) + conn.row_factory = sqlite3.Row + + # 检查是否有向量数据 + count = conn.execute("SELECT COUNT(*) FROM embeddings").fetchone()[0] + if count == 0: + # 无向量数据时使用关键词检索作为降级方案 + return keyword_fallback(conn, question) + + # 1. 将问题转为向量 + query_embeddings = get_embeddings([question]) + + # 2. 向量检索 + contexts = vector_search(conn, query_embeddings[0], top_k) + + if not contexts: + conn.close() + return { + "answer": "知识库中暂无与您问题相关的内容。请先运行采集和分析流程入库更多情报。", + "sources": [], + "contexts": [], + } + + # 3. LLM 生成回答 + result = generate_answer(question, contexts) + conn.close() + return result + + +def rag_qa_stream(question: str, top_k: int = 5): + """流式 RAG 问答:先检索,再流式生成。yield (type, data) 元组。""" + db_path = Path(os.getenv("CIBANK_DB", str(BASE_DIR / "data" / "cibank.db"))) + conn = sqlite3.connect(db_path) + conn.row_factory = sqlite3.Row + + count = conn.execute("SELECT COUNT(*) FROM embeddings").fetchone()[0] + if count == 0: + conn.close() + yield ("error", "知识库中暂无向量数据,请先运行采集和分析流程。") + return + + query_embeddings = get_embeddings([question]) + contexts = vector_search(conn, query_embeddings[0], top_k) + conn.close() + + if not contexts: + yield ("error", "知识库中暂无与您问题相关的内容。") + return + + sources = [ + {"title": ctx["title"], "source": ctx["source"], "date": ctx["date"]} + for ctx in contexts + ] + yield ("sources", sources) + + for token in generate_answer_stream(question, contexts): + yield ("token", token) + + +def keyword_fallback(conn: sqlite3.Connection, question: str) -> dict: + """无向量数据时的关键词检索降级方案。""" + rows = conn.execute( + """ + SELECT a.id, a.title, a.source_name, a.source_type, a.published_at, a.summary, + a.content, x.result_json + FROM articles a + LEFT JOIN ai_analyses x ON x.article_id = a.id AND x.analysis_type = 'editorial' + WHERE a.crawl_status = 'success' + AND (a.title LIKE ? OR a.summary LIKE ? OR a.content LIKE ?) + ORDER BY a.published_at DESC + LIMIT 5 + """, + (f"%{question}%", f"%{question}%", f"%{question}%"), + ).fetchall() + + if not rows: + conn.close() + return { + "answer": "知识库中暂无与您问题相关的内容。请先运行采集和分析流程入库更多情报。", + "sources": [], + "contexts": [], + } + + contexts = [] + for row in rows: + analysis = json.loads(row["result_json"]) if row["result_json"] else {} + contexts.append({ + "article_id": row["id"], + "title": row["title"], + "source": row["source_name"] or row["source_type"], + "date": (row["published_at"] or "")[:10], + "summary": analysis.get("summary") or row["summary"] or "", + "key_points": analysis.get("key_points", []), + "content": row["content"][:3000] if row["content"] else "", + "score": 0.5, + }) + conn.close() + return generate_answer(question, contexts) + + +if __name__ == "__main__": + import sys + question = " ".join(sys.argv[1:]) or "现在开茶饮店还有机会吗?" + result = rag_qa(question) + print(json.dumps(result, ensure_ascii=False, indent=2)) diff --git a/backend/requirements.txt b/backend/requirements.txt new file mode 100644 index 0000000..19faf79 --- /dev/null +++ b/backend/requirements.txt @@ -0,0 +1,5 @@ +playwright>=1.45,<2 +Flask>=3.0,<4 +flask-cors>=4.0,<5 +python-dotenv>=1.0,<2 +numpy>=1.26,<3 diff --git a/backend/run.sh b/backend/run.sh new file mode 100755 index 0000000..c8e1819 --- /dev/null +++ b/backend/run.sh @@ -0,0 +1,53 @@ +#!/bin/bash +# 餐智库一键启动脚本 +# 同时启动后端 API 服务和前端开发服务器 + +set -e + +BACKEND_DIR="$(cd "$(dirname "$0")" && pwd)" +FRONTEND_DIR="$(cd "$(dirname "$0")/.." && pwd)" + +echo "=== 餐智库启动 ===" + +# 检查后端虚拟环境 +if [ ! -d "$BACKEND_DIR/venv" ]; then + echo "[后端] 创建虚拟环境..." + python3 -m venv "$BACKEND_DIR/venv" + echo "[后端] 安装依赖..." + "$BACKEND_DIR/venv/bin/pip" install -r "$BACKEND_DIR/requirements.txt" -q + echo "[后端] 安装 Playwright 浏览器..." + "$BACKEND_DIR/venv/bin/playwright" install chromium +fi + +# 检查 .env 文件 +if [ ! -f "$BACKEND_DIR/.env" ]; then + echo "[后端] 复制 .env.example -> .env" + cp "$BACKEND_DIR/.env.example" "$BACKEND_DIR/.env" + echo "[提示] 请编辑 backend/.env 填入 DASHSCOPE_API_KEY" +fi + +# 启动后端 +echo "[后端] 启动 API 服务 (端口 8765)..." +"$BACKEND_DIR/venv/bin/python" "$BACKEND_DIR/app.py" & +BACKEND_PID=$! +echo "[后端] PID=$BACKEND_PID" + +# 等待后端就绪 +sleep 2 + +# 启动前端 +echo "[前端] 启动开发服务器 (端口 3000)..." +cd "$FRONTEND_DIR" +npm run dev & +FRONTEND_PID=$! +echo "[前端] PID=$FRONTEND_PID" + +echo "" +echo "=== 启动完成 ===" +echo "前端: http://localhost:3000" +echo "后端: http://localhost:8765/api/health" +echo "" +echo "按 Ctrl+C 停止所有服务" + +trap "kill $BACKEND_PID $FRONTEND_PID 2>/dev/null; exit" INT TERM +wait diff --git a/backend/schema.sql b/backend/schema.sql new file mode 100644 index 0000000..7239762 --- /dev/null +++ b/backend/schema.sql @@ -0,0 +1,154 @@ +PRAGMA journal_mode = WAL; +PRAGMA foreign_keys = ON; + +-- 文章表:公众号文章 + 视频号ASR转写文本 +CREATE TABLE IF NOT EXISTS articles ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + canonical_url TEXT NOT NULL UNIQUE, + source TEXT NOT NULL DEFAULT 'hongcan', -- 来源平台 + source_name TEXT, -- 公众号名称 / 视频号名称 + source_type TEXT NOT NULL DEFAULT '公众号', -- 公众号 | 视频号 + title TEXT, + author TEXT, + published_at TEXT, + summary TEXT, + content TEXT, + content_hash TEXT, + category TEXT, -- 赛道:茶饮咖啡/快餐/火锅/正餐/烘焙/供应链/综合 + crawl_status TEXT NOT NULL DEFAULT 'pending', -- pending | success | failed + retry_count INTEGER NOT NULL DEFAULT 0, + last_error TEXT, + media_url TEXT, -- 原始视频文件路径(视频号专用) + discovered_at TEXT NOT NULL, + crawled_at TEXT, + updated_at TEXT NOT NULL +); + +CREATE INDEX IF NOT EXISTS idx_articles_published_at ON articles(published_at); +CREATE INDEX IF NOT EXISTS idx_articles_crawl_status ON articles(crawl_status, retry_count); +CREATE INDEX IF NOT EXISTS idx_articles_source_type ON articles(source_type); +CREATE INDEX IF NOT EXISTS idx_articles_category ON articles(category); + +-- 采集运行记录 +CREATE TABLE IF NOT EXISTS crawl_runs ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + started_at TEXT NOT NULL, + finished_at TEXT, + status TEXT NOT NULL DEFAULT 'running', + source_type TEXT NOT NULL DEFAULT '公众号', + discovered_count INTEGER NOT NULL DEFAULT 0, + inserted_count INTEGER NOT NULL DEFAULT 0, + updated_count INTEGER NOT NULL DEFAULT 0, + failed_count INTEGER NOT NULL DEFAULT 0, + message TEXT +); + +-- AI 结构化分析结果 +CREATE TABLE IF NOT EXISTS ai_analyses ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + article_id INTEGER NOT NULL, + model TEXT NOT NULL, + analysis_type TEXT NOT NULL DEFAULT 'editorial', + result_json TEXT NOT NULL, -- 完整结构化JSON + content_hash TEXT, + created_at TEXT NOT NULL, + updated_at TEXT NOT NULL, + UNIQUE(article_id, model, analysis_type), + FOREIGN KEY(article_id) REFERENCES articles(id) ON DELETE CASCADE +); + +CREATE INDEX IF NOT EXISTS idx_ai_analyses_article ON ai_analyses(article_id, updated_at); + +-- 向量嵌入表(用于RAG检索) +CREATE TABLE IF NOT EXISTS embeddings ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + article_id INTEGER NOT NULL, + chunk_index INTEGER NOT NULL DEFAULT 0, + chunk_text TEXT NOT NULL, + embedding BLOB, -- JSON序列化的float数组 + model TEXT NOT NULL, + content_hash TEXT, + created_at TEXT NOT NULL, + FOREIGN KEY(article_id) REFERENCES articles(id) ON DELETE CASCADE, + UNIQUE(article_id, chunk_index, model) +); + +CREATE INDEX IF NOT EXISTS idx_embeddings_article ON embeddings(article_id); + +-- 品牌实体库 +CREATE TABLE IF NOT EXISTS brands ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + name TEXT NOT NULL UNIQUE, + category TEXT, + stores INTEGER DEFAULT 0, + avg_price INTEGER DEFAULT 0, + model TEXT, -- 直营 | 加盟 | 直营+加盟 + city_tier_json TEXT DEFAULT '[]', -- [{tier, pct}] + trend_json TEXT DEFAULT '[]', -- 近12个月门店数 + latest_news TEXT, + news_date TEXT, + growth REAL DEFAULT 0, + created_at TEXT NOT NULL, + updated_at TEXT NOT NULL +); + +-- 行业信号(跨文章聚合) +CREATE TABLE IF NOT EXISTS industry_signals ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + signal_date TEXT NOT NULL, + title TEXT NOT NULL, + summary TEXT NOT NULL, + trend TEXT NOT NULL DEFAULT 'emerging', + confidence REAL NOT NULL DEFAULT 0.5, + implications_json TEXT NOT NULL DEFAULT '[]', + article_ids_json TEXT NOT NULL DEFAULT '[]', + tags_json TEXT NOT NULL DEFAULT '[]', + model TEXT NOT NULL, + created_at TEXT NOT NULL, + updated_at TEXT NOT NULL, + UNIQUE(signal_date, title) +); + +CREATE INDEX IF NOT EXISTS idx_industry_signals_date ON industry_signals(signal_date DESC, confidence DESC); + +-- 报告表 +CREATE TABLE IF NOT EXISTS reports ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + title TEXT NOT NULL, + report_date TEXT NOT NULL, + period TEXT, + highlights_json TEXT NOT NULL DEFAULT '[]', + sections_json TEXT NOT NULL DEFAULT '[]', + created_at TEXT NOT NULL +); + +CREATE INDEX IF NOT EXISTS idx_reports_date ON reports(report_date DESC); + +CREATE TABLE IF NOT EXISTS pipeline_runs ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + run_type TEXT NOT NULL, + started_at TEXT NOT NULL, + finished_at TEXT, + status TEXT NOT NULL DEFAULT 'running', + current_step TEXT, + steps_json TEXT NOT NULL DEFAULT '{}', + error_message TEXT, + created_at TEXT NOT NULL +); + +CREATE INDEX IF NOT EXISTS idx_pipeline_runs_started ON pipeline_runs(started_at DESC); + +-- 信源配置表(公众号 + 视频号) +CREATE TABLE IF NOT EXISTS sources ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + source_type TEXT NOT NULL DEFAULT '公众号', -- 公众号 | 视频号 + name TEXT NOT NULL, -- 信源名称(公众号名称/视频号账号名) + finder TEXT, -- 视频号 finder ID(视频号专用) + list_url TEXT, -- 列表页URL(公众号专用) + download_dir TEXT, -- 下载目录(视频号专用) + enabled INTEGER NOT NULL DEFAULT 1, -- 是否启用 1/0 + last_crawled_at TEXT, -- 最近采集时间 + created_at TEXT NOT NULL, + updated_at TEXT NOT NULL, + UNIQUE(source_type, name) +); diff --git a/backend/structurer.py b/backend/structurer.py new file mode 100644 index 0000000..5b27e57 --- /dev/null +++ b/backend/structurer.py @@ -0,0 +1,693 @@ +#!/usr/bin/env python3 +"""结构化引擎:大模型自动打标签、摘要、提取要点、识别品牌实体。 + +对每篇采集入库的文章调用通义千问,输出结构化JSON: +- summary: 120字内摘要 +- key_points: 3-6条关键要点 +- tags: 5-10个标签 +- brands: 识别到的品牌实体 +- category: 赛道分类 +- type: 情报类型(政策监管/品牌动态/品类趋势/经营干货/供应链/消费洞察) +- score: 价值评分 0-100 +- timeliness: 时效性(高/中/低) +- sentiment: 情感倾向 +- content_angles: 可延展选题 +- risk_notes: 事实或表述风险 +- numbers: 关键数据 +""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import os +import sqlite3 +import time +import urllib.error +import urllib.request +from datetime import datetime, timezone +from pathlib import Path + +from dotenv import load_dotenv + +BASE_DIR = Path(__file__).resolve().parent +load_dotenv(BASE_DIR / ".env") + +DASHSCOPE_CHAT_URL = "https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions" +DASHSCOPE_EMBED_URL = "https://dashscope.aliyuncs.com/compatible-mode/v1/embeddings" + + +def now_iso() -> str: + return datetime.now(timezone.utc).astimezone().isoformat(timespec="seconds") + + +def get_api_key() -> str: + key = os.getenv("DASHSCOPE_API_KEY", "").strip() + if not key: + raise RuntimeError("尚未配置 DASHSCOPE_API_KEY,请在 .env 中设置") + return key + + +# ---- 单篇文章结构化分析 ---- + +def analyze_article(title: str, content: str) -> dict: + """调用通义千问对文章进行结构化分析。""" + api_key = get_api_key() + model = os.getenv("QWEN_MODEL", "qwen-plus") + prompt = f"""你是一名资深餐饮行业研究编辑。分析下面这篇文章,只返回合法 JSON,不要 Markdown 代码块。 +JSON 字段必须为: +- summary(120字内摘要) +- key_points(3-6条字符串,关键要点) +- tags(5-10个标签) +- brands(品牌数组,每个元素为对象:{{"name":"品牌名","stores":门店数整数或0,"avg_price":人均消费整数或0,"model":"直营/加盟/直营+加盟"}},仅填文中明确提及的数据,未提及的填0或空字符串) +- category(赛道分类,从以下选择:茶饮咖啡/快餐/火锅/正餐/烘焙/供应链/综合) +- type(情报类型,从以下选择:政策监管/品牌动态/品类趋势/经营干货/供应链/消费洞察) +- score(价值评分0-100整数,越高越有经营参考价值) +- timeliness(时效性:高/中/低) +- sentiment(positive/neutral/negative) +- content_angles(3条可延展选题) +- risk_notes(事实或表述风险数组) +- numbers(关键数据数组) + +文章标题:{title} +正文:{content[:24000]}""" + payload = json.dumps({ + "model": model, + "messages": [ + {"role": "system", "content": "你输出严谨、简洁、可供编辑部直接使用的结构化行业分析。"}, + {"role": "user", "content": prompt}, + ], + "temperature": 0.25, + "response_format": {"type": "json_object"}, + }, ensure_ascii=False).encode("utf-8") + req = urllib.request.Request( + DASHSCOPE_CHAT_URL, + data=payload, + headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}, + method="POST", + ) + try: + with urllib.request.urlopen(req, timeout=90) as response: + body = json.loads(response.read().decode("utf-8")) + except urllib.error.HTTPError as exc: + detail = exc.read().decode("utf-8", errors="replace")[:500] + raise RuntimeError(f"千问接口返回 {exc.code}:{detail}") from exc + if "choices" not in body or not body["choices"]: + raise RuntimeError(f"千问返回异常:{json.dumps(body, ensure_ascii=False)[:300]}") + text = body["choices"][0]["message"]["content"].strip() + if text.startswith("```"): + text = text.strip("`").removeprefix("json").strip() + try: + return json.loads(text) + except Exception as e: + raise RuntimeError(f"JSON解析失败: {e}\n{text[:200]}") from e + + +# ---- 跨文章行业信号提取 ---- + +def extract_industry_signals(items: list[dict], model: str) -> list[dict]: + """从多篇文章的结构化分析中提取跨文章行业信号。""" + api_key = get_api_key() + prompt = f"""你是餐饮产业首席分析师。根据以下多篇文章的结构化分析,识别跨文章、可验证、有经营意义的行业信号。 +只返回合法 JSON:{{"signals":[...]}}。每个 signal 必须包含: +- title(短标题) +- summary(100-180字) +- trend(emerging/accelerating/stable/declining) +- confidence(0到1) +- article_ids(至少2个证据文章ID) +- implications(2-4条经营启示) +- tags(3-6个标签) +不要把单一品牌新闻简单改写成行业信号;合并重复主题;最多输出8条。 + +输入:{json.dumps(items, ensure_ascii=False)}""" + body = json.dumps({ + "model": model, + "messages": [ + {"role": "system", "content": "你进行基于证据的餐饮行业趋势聚类,避免空泛结论。"}, + {"role": "user", "content": prompt}, + ], + "temperature": 0.2, + "response_format": {"type": "json_object"}, + }, ensure_ascii=False).encode("utf-8") + req = urllib.request.Request( + DASHSCOPE_CHAT_URL, + data=body, + headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}, + method="POST", + ) + try: + with urllib.request.urlopen(req, timeout=180) as response: + result = json.loads(response.read().decode("utf-8")) + except urllib.error.HTTPError as exc: + raise RuntimeError(f"千问接口返回 {exc.code}:{exc.read().decode('utf-8', errors='replace')[:500]}") from exc + except urllib.error.URLError as exc: + raise RuntimeError(f"千问接口网络超时:{exc}") from exc + text = result["choices"][0]["message"]["content"].strip() + if text.startswith("```"): + text = text.strip("`").removeprefix("json").strip() + return json.loads(text).get("signals", []) + + +# ---- 文本向量嵌入 ---- + +def get_embeddings(texts: list[str]) -> list[list[float]]: + """调用通义千问文本向量模型,批量获取向量。""" + api_key = get_api_key() + model = os.getenv("EMBEDDING_MODEL", "text-embedding-v3") + payload = json.dumps({ + "model": model, + "input": texts, + }, ensure_ascii=False).encode("utf-8") + req = urllib.request.Request( + DASHSCOPE_EMBED_URL, + data=payload, + headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}, + method="POST", + ) + try: + with urllib.request.urlopen(req, timeout=60) as response: + body = json.loads(response.read().decode("utf-8")) + except urllib.error.HTTPError as exc: + detail = exc.read().decode("utf-8", errors="replace")[:500] + raise RuntimeError(f"向量接口返回 {exc.code}:{detail}") from exc + if "output" in body: + return [item["embedding"] for item in body["output"]["embeddings"]] + return [item["embedding"] for item in body["data"]] + + +# ---- 品牌实体入库 ---- + +def upsert_brand(conn: sqlite3.Connection, brand_name: str, category: str | None = None, + stores: int = 0, avg_price: int = 0, model: str | None = None, + latest_news: str | None = None, news_date: str | None = None) -> None: + """将识别到的品牌实体写入品牌表,有新数据时更新。""" + existing = conn.execute("SELECT id, stores, avg_price FROM brands WHERE name=?", (brand_name,)).fetchone() + timestamp = now_iso() + if existing: + # 只在新值更大或非空时更新,避免旧数据被覆盖 + new_stores = max(existing["stores"], stores) if stores > 0 else existing["stores"] + new_avg_price = avg_price if avg_price > 0 else existing["avg_price"] + conn.execute( + """ + UPDATE brands SET category=COALESCE(?, category), + stores=?, avg_price=?, + model=COALESCE(NULLIF(?, ''), model), + latest_news=COALESCE(NULLIF(?, ''), latest_news), + news_date=COALESCE(NULLIF(?, ''), news_date), + updated_at=? + WHERE id=? + """, + (category, new_stores, new_avg_price, model or "", latest_news or "", news_date or "", timestamp, existing["id"]), + ) + else: + conn.execute( + """ + INSERT INTO brands (name, category, stores, avg_price, model, city_tier_json, trend_json, + latest_news, news_date, growth, created_at, updated_at) + VALUES (?, ?, ?, ?, NULLIF(?, ''), '[]', '[]', NULLIF(?, ''), NULLIF(?, ''), 0, ?, ?) + """, + (brand_name, category, stores, avg_price, model or "", latest_news or "", news_date or "", timestamp, timestamp), + ) + conn.commit() + + +# ---- 批量分析主流程 ---- + +def run_batch_analyze(force: bool, pause: float, retries: int) -> int: + """批量对所有成功采集的文章执行结构化分析。""" + db_path = Path(os.getenv("CIBANK_DB", str(BASE_DIR / "data" / "cibank.db"))) + conn = sqlite3.connect(db_path) + conn.row_factory = sqlite3.Row + conn.executescript((BASE_DIR / "schema.sql").read_text(encoding="utf-8")) + + model = os.getenv("QWEN_MODEL", "qwen-plus") + rows = conn.execute( + """ + SELECT a.id, a.title, a.content, a.content_hash, a.published_at, x.content_hash analyzed_hash + FROM articles a + LEFT JOIN ai_analyses x + ON x.article_id = a.id AND x.model = ? AND x.analysis_type = 'editorial' + WHERE a.crawl_status = 'success' + ORDER BY a.published_at DESC, a.id DESC + """, + (model,), + ).fetchall() + + pending = [r for r in rows if force or not r["analyzed_hash"] or r["analyzed_hash"] != r["content_hash"]] + print(json.dumps({"total": len(rows), "pending": len(pending), "model": model}, ensure_ascii=False), flush=True) + + success = skipped = failed = 0 + for index, row in enumerate(rows, start=1): + if not force and row["analyzed_hash"] == row["content_hash"]: + skipped += 1 + continue + last_error = None + for attempt in range(1, retries + 2): + try: + result = analyze_article(row["title"], row["content"]) + timestamp = now_iso() + conn.execute( + """ + INSERT INTO ai_analyses(article_id, model, analysis_type, result_json, content_hash, created_at, updated_at) + VALUES (?, ?, 'editorial', ?, ?, ?, ?) + ON CONFLICT(article_id, model, analysis_type) DO UPDATE SET + result_json = excluded.result_json, content_hash = excluded.content_hash, updated_at = excluded.updated_at + """, + (row["id"], model, json.dumps(result, ensure_ascii=False), row["content_hash"], timestamp, timestamp), + ) + # 更新文章的category字段 + if result.get("category"): + conn.execute("UPDATE articles SET category=? WHERE id=?", (result["category"], row["id"])) + + # 品牌实体入库 + for brand in result.get("brands", []): + if isinstance(brand, str): + upsert_brand(conn, brand, result.get("category")) + elif isinstance(brand, dict): + upsert_brand(conn, brand.get("name", ""), result.get("category"), + int(brand.get("stores", 0) or 0), int(brand.get("avg_price", 0) or 0), + brand.get("model"), row["title"], (row["published_at"] or "")[:10]) + + conn.commit() + success += 1 + print(f"[{index}/{len(rows)}] OK {row['title']}", flush=True) + last_error = None + break + except Exception as exc: + last_error = exc + if attempt <= retries: + wait = attempt * 2 + print(f"[{index}/{len(rows)}] RETRY {attempt}/{retries} {exc}", flush=True) + time.sleep(wait) + if last_error is not None: + failed += 1 + print(f"[{index}/{len(rows)}] FAIL {row['title']}: {last_error}", flush=True) + time.sleep(pause) + + total_analyzed = conn.execute("SELECT COUNT(DISTINCT article_id) FROM ai_analyses").fetchone()[0] + conn.close() + print(json.dumps({ + "success": success, "skipped": skipped, "failed": failed, + "total_analyzed": total_analyzed, + }, ensure_ascii=False), flush=True) + return 0 if failed == 0 else 1 + + +def run_extract_signals(days: int) -> int: + """提取跨文章行业信号。""" + db_path = Path(os.getenv("CIBANK_DB", str(BASE_DIR / "data" / "cibank.db"))) + conn = sqlite3.connect(db_path, timeout=30) + conn.row_factory = sqlite3.Row + conn.executescript((BASE_DIR / "schema.sql").read_text(encoding="utf-8")) + + model = os.getenv("QWEN_MODEL", "qwen-plus") + from datetime import datetime, timedelta + cutoff = (datetime.now() - timedelta(days=days)).strftime("%Y-%m-%dT%H:%M:%S") + rows = conn.execute( + """ + SELECT a.id, a.title, a.published_at, x.result_json + FROM articles a JOIN ai_analyses x ON x.article_id = a.id + WHERE a.crawl_status = 'success' AND a.published_at >= ? + ORDER BY a.published_at DESC + """, + (cutoff,), + ).fetchall() + + items = [] + for row in rows: + analysis = json.loads(row["result_json"]) + items.append({ + "article_id": row["id"], "title": row["title"], "published_at": row["published_at"], + "summary": analysis.get("summary"), "key_points": analysis.get("key_points", []), + "type": analysis.get("type"), "tags": analysis.get("tags", []), + "brands": analysis.get("brands", []), "score": analysis.get("score", 0), + }) + + if not items: + print(json.dumps({"status": "skipped", "reason": "没有已分析文章"}, ensure_ascii=False)) + return 0 + + # 分批提取信号,避免一次性传入过多内容触发内容审查 + signal_date = datetime.now().astimezone().date().isoformat() + timestamp = now_iso() + conn.execute("DELETE FROM industry_signals WHERE signal_date=?", (signal_date,)) + conn.commit() + + all_signals = [] + batch_size = 20 + total_batches = (len(items) + batch_size - 1) // batch_size + for i in range(0, len(items), batch_size): + batch_num = i // batch_size + 1 + batch = items[i:i + batch_size] + print(f" 信号批次 {batch_num}/{total_batches},{len(batch)} 篇", flush=True) + for attempt in range(2): + try: + batch_signals = extract_industry_signals(batch, model) + all_signals.extend(batch_signals) + # 每批成功后立即写入数据库 + for signal in batch_signals: + conn.execute( + """ + INSERT OR IGNORE INTO industry_signals + (signal_date, title, summary, trend, confidence, implications_json, article_ids_json, tags_json, model, created_at, updated_at) + VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) + """, + ( + signal_date, signal["title"], signal["summary"], signal.get("trend", "emerging"), + float(signal.get("confidence", 0.5)), json.dumps(signal.get("implications", []), ensure_ascii=False), + json.dumps(signal.get("article_ids", [])), json.dumps(signal.get("tags", []), ensure_ascii=False), + model, timestamp, timestamp, + ), + ) + conn.commit() + break + except RuntimeError as exc: + print(f" 批次 {batch_num} 失败(尝试{attempt+1}): {exc}", flush=True) + if attempt == 0: + import time; time.sleep(2) + continue + except Exception as exc: + print(f" 批次 {batch_num} 异常(尝试{attempt+1}): {type(exc).__name__}: {exc}", flush=True) + if attempt == 0: + import time; time.sleep(2) + continue + + # 合并去重:按标题去重,保留置信度更高的 + seen = {} + for sig in all_signals: + title = sig.get("title", "") + conf = float(sig.get("confidence", 0)) + if title not in seen or conf > float(seen[title].get("confidence", 0)): + seen[title] = sig + signals = list(seen.values())[:8] + + # 用去重后的结果替换当天信号 + conn.execute("DELETE FROM industry_signals WHERE signal_date=?", (signal_date,)) + for signal in signals: + conn.execute( + """ + INSERT INTO industry_signals + (signal_date, title, summary, trend, confidence, implications_json, article_ids_json, tags_json, model, created_at, updated_at) + VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) + """, + ( + signal_date, signal["title"], signal["summary"], signal.get("trend", "emerging"), + float(signal.get("confidence", 0.5)), json.dumps(signal.get("implications", []), ensure_ascii=False), + json.dumps(signal.get("article_ids", [])), json.dumps(signal.get("tags", []), ensure_ascii=False), + model, timestamp, timestamp, + ), + ) + conn.commit() + conn.close() + print(json.dumps({"status": "success", "input_articles": len(items), "signals": len(signals), "date": signal_date}, ensure_ascii=False)) + return 0 + + +# ---- 向量嵌入生成 ---- + +def ensure_embedding_schema(conn: sqlite3.Connection) -> None: + """兼容已有数据库,为向量表补齐增量更新所需字段与唯一索引。""" + columns = {row[1] for row in conn.execute("PRAGMA table_info(embeddings)").fetchall()} + if "content_hash" not in columns: + conn.execute("ALTER TABLE embeddings ADD COLUMN content_hash TEXT") + conn.execute( + """ + DELETE FROM embeddings + WHERE id NOT IN ( + SELECT MAX(id) FROM embeddings GROUP BY article_id, chunk_index, model + ) + """ + ) + conn.execute( + "CREATE UNIQUE INDEX IF NOT EXISTS idx_embeddings_unique_chunk " + "ON embeddings(article_id, chunk_index, model)" + ) + conn.commit() + + +def split_text(text: str, chunk_size: int = 800, overlap: int = 120) -> list[str]: + """将长文本切分为有重叠的中文检索片段。""" + normalized = "\n".join(line.strip() for line in text.splitlines() if line.strip()) + if not normalized: + return [] + chunks = [] + start = 0 + while start < len(normalized): + end = min(len(normalized), start + chunk_size) + if end < len(normalized): + candidates = [normalized.rfind(mark, start + chunk_size // 2, end) for mark in "。!?;\n"] + boundary = max(candidates) + if boundary > start: + end = boundary + 1 + chunks.append(normalized[start:end]) + if end >= len(normalized): + break + start = max(start + 1, end - overlap) + return chunks + + +def build_embedding_chunks(row: sqlite3.Row) -> tuple[list[str], str]: + """构建包含标题、AI摘要和完整原文的向量分块。""" + analysis = json.loads(row["result_json"]) if row["result_json"] else {} + header_parts = [f"标题:{row['title'] or ''}"] + summary = analysis.get("summary") or row["summary"] or "" + if summary: + header_parts.append(f"摘要:{summary}") + key_points = analysis.get("key_points") or [] + if key_points: + header_parts.append("要点:" + ";".join(str(item) for item in key_points)) + tags = analysis.get("tags") or [] + if tags: + header_parts.append("标签:" + "、".join(str(item) for item in tags)) + header = "\n".join(header_parts) + content_chunks = split_text(row["content"] or "") or [""] + chunks = [f"{header}\n正文片段:{content}".strip() for content in content_chunks] + source_hash = hashlib.sha256("\n\n".join(chunks).encode("utf-8")).hexdigest() + return chunks, source_hash + + +def run_generate_embeddings(force: bool) -> int: + """为所有成功文章增量生成分块向量,用于RAG检索。""" + db_path = Path(os.getenv("CIBANK_DB", str(BASE_DIR / "data" / "cibank.db"))) + conn = sqlite3.connect(db_path) + conn.row_factory = sqlite3.Row + conn.executescript((BASE_DIR / "schema.sql").read_text(encoding="utf-8")) + ensure_embedding_schema(conn) + + model = os.getenv("EMBEDDING_MODEL", "text-embedding-v3") + rows = conn.execute( + """ + SELECT a.id, a.title, a.summary, a.content, x.result_json + FROM articles a + LEFT JOIN ai_analyses x ON x.id = ( + SELECT x2.id FROM ai_analyses x2 + WHERE x2.article_id = a.id AND x2.analysis_type = 'editorial' + ORDER BY x2.updated_at DESC, x2.id DESC LIMIT 1 + ) + WHERE a.crawl_status = 'success' + ORDER BY a.id + """, + ).fetchall() + + success = skipped = failed = chunks_written = 0 + for row in rows: + chunks, source_hash = build_embedding_chunks(row) + existing = conn.execute( + "SELECT content_hash FROM embeddings WHERE article_id=? AND model=? LIMIT 1", + (row["id"], model), + ).fetchone() + if not force and existing and existing["content_hash"] == source_hash: + skipped += 1 + continue + + try: + vectors = [] + for start in range(0, len(chunks), 10): + vectors.extend(get_embeddings(chunks[start:start + 10])) + if len(vectors) != len(chunks): + raise RuntimeError(f"向量数量异常:期望 {len(chunks)},实际 {len(vectors)}") + timestamp = now_iso() + conn.execute("BEGIN") + conn.execute("DELETE FROM embeddings WHERE article_id=? AND model=?", (row["id"], model)) + conn.executemany( + """ + INSERT INTO embeddings + (article_id, chunk_index, chunk_text, embedding, model, content_hash, created_at) + VALUES (?, ?, ?, ?, ?, ?, ?) + """, + [ + (row["id"], index, chunk, json.dumps(vector), model, source_hash, timestamp) + for index, (chunk, vector) in enumerate(zip(chunks, vectors)) + ], + ) + conn.commit() + success += 1 + chunks_written += len(chunks) + except Exception as exc: + conn.rollback() + failed += 1 + print(f"FAIL article {row['id']}: {exc}", flush=True) + + conn.close() + print(json.dumps({ + "success": success, "skipped": skipped, "failed": failed, + "chunks_written": chunks_written, "model": model, + }, ensure_ascii=False)) + return 0 if failed == 0 else 1 + + +def run_generate_report(days: int = 7) -> int: + """生成周期报告:聚合近期文章分析结果,调用LLM生成结构化报告。""" + db_path = Path(os.getenv("CIBANK_DB", str(BASE_DIR / "data" / "cibank.db"))) + conn = sqlite3.connect(db_path, timeout=30) + conn.row_factory = sqlite3.Row + conn.executescript((BASE_DIR / "schema.sql").read_text(encoding="utf-8")) + + model = os.getenv("QWEN_MODEL", "qwen-plus") + from datetime import datetime, timedelta + cutoff = (datetime.now() - timedelta(days=days)).strftime("%Y-%m-%d") + + rows = conn.execute( + """ + SELECT a.id, a.title, a.source_name, a.published_at, a.category, + x.result_json + FROM articles a + JOIN ai_analyses x ON x.article_id = a.id AND x.analysis_type = 'editorial' + WHERE a.crawl_status = 'success' AND a.published_at >= ? + ORDER BY a.published_at DESC + LIMIT 50 + """, + (cutoff,), + ).fetchall() + + if not rows: + print(json.dumps({"status": "skip", "reason": "近期无分析数据"}, ensure_ascii=False)) + return 0 + + items = [] + for r in rows: + analysis = json.loads(r["result_json"]) if r["result_json"] else {} + items.append({ + "title": r["title"], + "source": r["source_name"] or "", + "date": (r["published_at"] or "")[:10], + "category": r["category"] or analysis.get("category", ""), + "type": analysis.get("type", ""), + "summary": analysis.get("summary", ""), + "key_points": analysis.get("key_points", [])[:3], + "score": analysis.get("score", 0), + "brands": [b["name"] if isinstance(b, dict) else b for b in analysis.get("brands", [])][:3], + }) + + api_key = get_api_key() + period_label = f"近{days}天" + prompt = f"""你是餐饮行业首席分析师。根据以下{len(items)}篇情报的结构化分析,生成一份{period_label}餐饮行业经营决策简报。 +只返回合法 JSON,不要 Markdown 代码块。格式: +{{ + "title": "报告标题(含日期范围)", + "highlights": ["核心要点1", "核心要点2", "核心要点3", "核心要点4"], + "sections": [ + {{"heading": "板块标题", "body": "详细分析正文(200-400字)"}}, + {{"heading": "板块标题", "body": "详细分析正文(200-400字)"}}, + {{"heading": "板块标题", "body": "详细分析正文(200-400字)"}}, + {{"heading": "经营建议", "body": "基于以上分析的经营建议(200-300字)"}} + ] +}} + +要求: +1. highlights 为4-6条核心洞察,每条15-30字 +2. sections 至少4个板块,覆盖赛道趋势、品牌动态、政策/供应链、经营建议 +3. 内容必须基于提供的情报数据,不要编造 +4. 语言专业、简洁、有决策参考价值 + +情报数据: +{json.dumps(items, ensure_ascii=False)}""" + + payload = json.dumps({ + "model": model, + "messages": [ + {"role": "system", "content": "你是餐饮行业首席分析师,输出严谨的经营决策简报。"}, + {"role": "user", "content": prompt}, + ], + "temperature": 0.3, + "response_format": {"type": "json_object"}, + }, ensure_ascii=False).encode("utf-8") + + req = urllib.request.Request( + DASHSCOPE_CHAT_URL, + data=payload, + headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}, + method="POST", + ) + try: + with urllib.request.urlopen(req, timeout=120) as response: + body = json.loads(response.read().decode("utf-8")) + except urllib.error.HTTPError as exc: + detail = exc.read().decode("utf-8", errors="replace")[:500] + print(json.dumps({"status": "error", "error": f"千问接口返回 {exc.code}:{detail}"}, ensure_ascii=False)) + return 1 + + text = body["choices"][0]["message"]["content"].strip() + if text.startswith("```"): + text = text.strip("`").removeprefix("json").strip() + result = json.loads(text) + + report_date = datetime.now().strftime("%Y-%m-%d") + timestamp = now_iso() + conn.execute( + """ + INSERT INTO reports (title, report_date, period, highlights_json, sections_json, created_at) + VALUES (?, ?, ?, ?, ?, ?) + """, + ( + result["title"], + report_date, + period_label, + json.dumps(result["highlights"], ensure_ascii=False), + json.dumps(result["sections"], ensure_ascii=False), + timestamp, + ), + ) + conn.commit() + conn.close() + + print(json.dumps({ + "status": "success", + "title": result["title"], + "date": report_date, + "highlights": len(result["highlights"]), + "sections": len(result["sections"]), + }, ensure_ascii=False)) + return 0 + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="结构化引擎:LLM分析+品牌识别+信号提取+向量嵌入") + sub = parser.add_subparsers(dest="command") + + p_analyze = sub.add_parser("analyze", help="批量文章结构化分析") + p_analyze.add_argument("--force", action="store_true") + p_analyze.add_argument("--pause", type=float, default=0.35) + p_analyze.add_argument("--retries", type=int, default=2) + + p_signals = sub.add_parser("signals", help="提取跨文章行业信号") + p_signals.add_argument("--days", type=int, default=30) + + p_embed = sub.add_parser("embed", help="生成向量嵌入") + p_embed.add_argument("--force", action="store_true") + + p_report = sub.add_parser("report", help="生成周期报告") + p_report.add_argument("--days", type=int, default=7) + + args = parser.parse_args() + if args.command == "analyze": + raise SystemExit(run_batch_analyze(args.force, args.pause, args.retries)) + elif args.command == "signals": + raise SystemExit(run_extract_signals(args.days)) + elif args.command == "embed": + raise SystemExit(run_generate_embeddings(args.force)) + elif args.command == "report": + raise SystemExit(run_generate_report(args.days)) + else: + parser.print_help() diff --git a/backend/video_config.json b/backend/video_config.json new file mode 100644 index 0000000..6cc8ddd --- /dev/null +++ b/backend/video_config.json @@ -0,0 +1,14 @@ +{ + "sources": [ + { + "account": "剑哥聊餐饮", + "finder": "sphnu5kSqZT224x", + "directory": "/Volumes/Projects/视频/剑哥聊餐饮" + }, + { + "account": "餐饮周伯通(赖老师TimLai)", + "finder": "sph0XoCC1rhJXB7", + "directory": "/Volumes/Projects/视频/餐饮周伯通" + } + ] +} diff --git a/backend/video_downloader.py b/backend/video_downloader.py new file mode 100644 index 0000000..70f4ab4 --- /dev/null +++ b/backend/video_downloader.py @@ -0,0 +1,236 @@ +#!/usr/bin/env python3 +"""视频号批量下载管理器。 + +基于 wx_channels_download 工具的 API 接口,实现: +1. 检查工具是否运行 +2. 获取视频号创作者的视频列表 +3. 与本地已下载文件比对,发现新视频 +4. 触发批量下载 +5. 下载完成后自动触发 ASR 转写入库 + +工具 GitHub: https://github.com/ltaoo/wx_channels_download +API 基地址: http://127.0.0.1:2022 + +使用前提: +- wx_channels_download 以 sudo 运行 +- IPv6 已禁用(sudo networksetup -setv6off Wi-Fi) +- VPN 已关闭 +- 微信保持运行 +""" + +from __future__ import annotations + +import argparse +import json +import os +import re +import sys +import time +import urllib.error +import urllib.request +from datetime import datetime, timezone +from pathlib import Path +from typing import Any + +from dotenv import load_dotenv + +BASE_DIR = Path(__file__).resolve().parent +load_dotenv(BASE_DIR / ".env") + +TOOL_API = os.getenv("WX_VIDEO_API", "http://127.0.0.1:2022") +VIDEO_CONFIG_PATH = BASE_DIR / "video_config.json" + + +def now_iso() -> str: + return datetime.now(timezone.utc).astimezone().isoformat(timespec="seconds") + + +def api_get(endpoint: str, params: dict[str, str] | None = None, timeout: int = 30) -> dict[str, Any]: + """调用 wx_channels_download 工具的 API。""" + url = f"{TOOL_API}{endpoint}" + if params: + qs = "&".join(f"{k}={v}" for k, v in params.items()) + url += f"?{qs}" + req = urllib.request.Request(url, method="GET") + try: + with urllib.request.urlopen(req, timeout=timeout) as response: + return json.loads(response.read().decode("utf-8")) + except urllib.error.HTTPError as exc: + detail = exc.read().decode("utf-8", errors="replace")[:500] + raise RuntimeError(f"工具 API 返回 {exc.code}:{detail}") from exc + except urllib.error.URLError as exc: + raise RuntimeError(f"无法连接工具 API({TOOL_API}):{exc.reason}\n请确认 wx_channels_download 已以 sudo 启动。") from exc + + +def check_tool_running() -> bool: + """检查下载工具是否在运行。""" + try: + result = api_get("/api/channels/version", timeout=5) + return bool(result.get("version") or result.get("data")) + except Exception: + return False + + +def get_video_list(finder: str) -> list[dict[str, Any]]: + """获取视频号创作者的视频列表。 + + 需要微信客户端已连接到工具(WebSocket)。 + """ + result = api_get("/api/channels/contact/feed/list", {"finder": finder}, timeout=60) + # 适配不同版本的返回格式 + if isinstance(result, list): + return result + if isinstance(result, dict): + return result.get("data", result.get("list", result.get("feeds", []))) + return [] + + +def list_local_videos(directory: Path) -> set[str]: + """列出本地已下载的视频文件名(不含扩展名)。""" + if not directory.is_dir(): + return set() + suffixes = {".mp4", ".mov", ".m4v", ".mkv", ".webm"} + return {f.stem for f in directory.iterdir() if f.is_file() and f.suffix.lower() in suffixes} + + +def normalize_title(title: str) -> str: + """将视频标题规范化,用于与本地文件名比对。""" + # 去除 wx_channels_download 添加的 _xWT 后缀和画质标记 + value = re.sub(r"[_ ]?xWT\d+$", "", title, flags=re.I) + value = re.sub(r"[\x00-\x1f/:]", "_", value).strip(" .") + return value[:180] or "untitled" + + +def find_new_videos(video_list: list[dict[str, Any]], local_dir: Path) -> list[dict[str, Any]]: + """比对线上视频列表与本地已下载文件,返回未下载的视频。""" + local_names = list_local_videos(local_dir) + new_videos = [] + for video in video_list: + title = video.get("title", video.get("desc", "")) + normalized = normalize_title(title) + # 检查本地是否已有该视频(模糊匹配) + if not any(normalized in local_name or local_name in normalized for local_name in local_names): + new_videos.append(video) + return new_videos + + +def create_batch_download(finder: str, video_ids: list[str]) -> dict[str, Any]: + """创建批量下载任务。""" + params = {"finder": finder, "ids": ",".join(video_ids)} + return api_get("/api/task/create_batch", params, timeout=120) + + +def wait_for_downloads(local_dir: Path, expected_count: int, timeout: int = 600) -> int: + """等待下载完成,返回实际新增的文件数。""" + initial = list_local_videos(local_dir) + deadline = time.time() + timeout + while time.time() < deadline: + time.sleep(10) + current = list_local_videos(local_dir) + new_files = current - initial + if len(new_files) >= expected_count: + return len(new_files) + return len(list_local_videos(local_dir) - initial) + + +def run_download(auto_transcribe: bool = True) -> int: + """主流程:检查工具 → 获取视频列表 → 发现新视频 → 触发下载。""" + config = json.loads(VIDEO_CONFIG_PATH.read_text(encoding="utf-8")) if VIDEO_CONFIG_PATH.exists() else {"sources": []} + sources = config.get("sources", []) + + if not sources: + print(json.dumps({"status": "skipped", "reason": "video_config.json 中未配置视频源"}, ensure_ascii=False)) + return 0 + + # 1. 检查工具是否运行 + if not check_tool_running(): + print(json.dumps({ + "status": "error", + "reason": f"wx_channels_download 工具未运行({TOOL_API})", + "hint": "请执行: sudo <工具路径>/wx_video_download,并确保 IPv6 已禁用、VPN 已关闭" + }, ensure_ascii=False)) + return 1 + + print(json.dumps({"status": "tool_running", "api": TOOL_API}, ensure_ascii=False), flush=True) + + total_new = 0 + total_downloaded = 0 + + for source in sources: + account = source.get("account", "未知") + finder = source.get("finder", "") + download_dir = Path(source.get("directory", "")) + + if not finder: + print(f"SKIP {account}: 未配置 finder", file=sys.stderr) + continue + + # 2. 获取视频列表 + try: + video_list = get_video_list(finder) + except Exception as exc: + print(f"FAIL {account}: 获取视频列表失败 - {exc}", file=sys.stderr, flush=True) + continue + + print(f"INFO {account}: 线上视频 {len(video_list)} 个", flush=True) + + # 3. 发现新视频 + new_videos = find_new_videos(video_list, download_dir) + total_new += len(new_videos) + + if not new_videos: + print(f"INFO {account}: 无新视频", flush=True) + continue + + print(f"INFO {account}: 发现 {len(new_videos)} 个新视频", flush=True) + + # 4. 触发批量下载 + video_ids = [v.get("id", v.get("objectId", "")) for v in new_videos if v.get("id") or v.get("objectId")] + if video_ids: + try: + result = create_batch_download(finder, video_ids) + print(f"INFO {account}: 批量下载任务已创建 - {result}", flush=True) + # 5. 等待下载完成 + downloaded = wait_for_downloads(download_dir, len(new_videos), timeout=600) + total_downloaded += downloaded + print(f"INFO {account}: 新增下载 {downloaded} 个文件", flush=True) + except Exception as exc: + print(f"FAIL {account}: 下载失败 - {exc}", file=sys.stderr, flush=True) + else: + # 如果无法获取 video_id,提示用户手动下载 + print(f"WARN {account}: 无法自动获取视频ID,请通过微信内批量下载或 Web 管理页面手动下载", flush=True) + print(f" Web 管理页面: {TOOL_API}/download", flush=True) + print(f" 下载目录: {download_dir}", flush=True) + + print(json.dumps({ + "status": "success", + "total_new": total_new, + "total_downloaded": total_downloaded, + }, ensure_ascii=False), flush=True) + + # 6. 自动触发 ASR 转写入库 + if auto_transcribe and total_downloaded > 0: + print("INFO 自动触发视频号 ASR 转写...", flush=True) + import subprocess + subprocess.run( + [sys.executable, str(BASE_DIR / "crawler.py"), "--source-type", "视频号"], + cwd=str(BASE_DIR), + ) + + return 0 + + +def build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser(description="视频号批量下载管理器") + parser.add_argument("--no-transcribe", action="store_true", help="仅下载,不自动触发 ASR 转写") + parser.add_argument("--check", action="store_true", help="仅检查工具状态") + return parser + + +if __name__ == "__main__": + args = build_parser().parse_args() + if args.check: + running = check_tool_running() + print(json.dumps({"tool_running": running, "api": TOOL_API}, ensure_ascii=False)) + raise SystemExit(0 if running else 1) + raise SystemExit(run_download(auto_transcribe=not args.no_transcribe)) diff --git a/backend/video_sources.json b/backend/video_sources.json new file mode 100644 index 0000000..5ce8da4 --- /dev/null +++ b/backend/video_sources.json @@ -0,0 +1,6 @@ +{ + "videos": [ + {"url": "https://example.com/video/1", "title": "海底捞创始人分享:翻台率不是目标", "source_name": "海底捞官方视频号"}, + {"url": "https://example.com/video/2", "title": "西贝贾国龙谈预制菜争议", "source_name": "餐饮O2O视频号"} + ] +} diff --git a/components.json b/components.json new file mode 100644 index 0000000..411f770 --- /dev/null +++ b/components.json @@ -0,0 +1,22 @@ +{ + "$schema": "https://ui.shadcn.com/schema.json", + "style": "new-york", + "rsc": false, + "tsx": true, + "tailwind": { + "config": "postcss.config.js", + "css": "src/index.css", + "baseColor": "slate", + "cssVariables": true, + "prefix": "" + }, + "iconLibrary": "lucide", + "aliases": { + "components": "@/components", + "utils": "@/lib/utils", + "ui": "@/components/ui", + "lib": "@/lib", + "hooks": "@/hooks" + }, + "registries": {} +} diff --git a/eslint.config.js b/eslint.config.js new file mode 100644 index 0000000..5e6b472 --- /dev/null +++ b/eslint.config.js @@ -0,0 +1,23 @@ +import js from '@eslint/js' +import globals from 'globals' +import reactHooks from 'eslint-plugin-react-hooks' +import reactRefresh from 'eslint-plugin-react-refresh' +import tseslint from 'typescript-eslint' +import { defineConfig, globalIgnores } from 'eslint/config' + +export default defineConfig([ + globalIgnores(['dist']), + { + files: ['**/*.{ts,tsx}'], + extends: [ + js.configs.recommended, + tseslint.configs.recommended, + reactHooks.configs.flat.recommended, + reactRefresh.configs.vite, + ], + languageOptions: { + ecmaVersion: 2020, + globals: globals.browser, + }, + }, +]) diff --git a/index.html b/index.html new file mode 100644 index 0000000..9243b46 --- /dev/null +++ b/index.html @@ -0,0 +1,12 @@ + + + + + + 餐智库 — 餐饮行业知识库 + + +
+ + + diff --git a/info.md b/info.md new file mode 100644 index 0000000..bccea11 --- /dev/null +++ b/info.md @@ -0,0 +1,31 @@ +Using Node.js 20, Tailwind CSS v3.4.19, and Vite v7.2.4 + +Tailwind CSS has been set up with the shadcn theme + +Setup complete: /mnt/agents/output/app + +Components (40+): + accordion, alert-dialog, alert, aspect-ratio, avatar, badge, breadcrumb, + button-group, button, calendar, card, carousel, chart, checkbox, collapsible, + command, context-menu, dialog, drawer, dropdown-menu, empty, field, form, + hover-card, input-group, input-otp, input, item, kbd, label, menubar, + navigation-menu, pagination, popover, progress, radio-group, resizable, + scroll-area, select, separator, sheet, sidebar, skeleton, slider, sonner, + spinner, switch, table, tabs, textarea, toggle-group, toggle, tooltip + +Usage: + import { Button } from '@/components/ui/button' + import { Card, CardHeader, CardTitle } from '@/components/ui/card' + +Structure: + src/sections/ Page sections + src/hooks/ Custom hooks + src/types/ Type definitions + src/App.css Styles specific to the Webapp + src/App.tsx Root React component + src/index.css Global styles + src/main.tsx Entry point for rendering the Webapp + index.html Entry point for the Webapp + tailwind.config.js Configures Tailwind's theme, plugins, etc. + vite.config.ts Main build and dev server settings for Vite + postcss.config.js Config file for CSS post-processing tools \ No newline at end of file diff --git a/package-lock.json b/package-lock.json new file mode 100644 index 0000000..4903d0b --- /dev/null +++ b/package-lock.json @@ -0,0 +1,9727 @@ +{ + "name": "my-app", + "version": "0.0.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "my-app", + "version": "0.0.0", + "dependencies": { + "@hookform/resolvers": "^5.2.2", + "@radix-ui/react-accordion": "^1.2.12", + "@radix-ui/react-alert-dialog": "^1.1.15", + "@radix-ui/react-aspect-ratio": "^1.1.8", + "@radix-ui/react-avatar": 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"^1.0.7", + "tw-animate-css": "^1.4.0", + "typescript": "~5.9.3", + "typescript-eslint": "^8.46.4", + "vite": "^7.2.4" + } +} diff --git a/postcss.config.js b/postcss.config.js new file mode 100644 index 0000000..2e7af2b --- /dev/null +++ b/postcss.config.js @@ -0,0 +1,6 @@ +export default { + plugins: { + tailwindcss: {}, + autoprefixer: {}, + }, +} diff --git a/src/App.css b/src/App.css new file mode 100644 index 0000000..b9d355d --- /dev/null +++ b/src/App.css @@ -0,0 +1,42 @@ +#root { + max-width: 1280px; + margin: 0 auto; + padding: 2rem; + text-align: center; +} + +.logo { + height: 6em; + padding: 1.5em; + will-change: filter; + transition: filter 300ms; +} +.logo:hover { + filter: drop-shadow(0 0 2em #646cffaa); +} +.logo.react:hover { + filter: drop-shadow(0 0 2em #61dafbaa); +} + +@keyframes logo-spin { + from { + transform: rotate(0deg); + } + to { + transform: rotate(360deg); + } +} + +@media (prefers-reduced-motion: no-preference) { + a:nth-of-type(2) .logo { + animation: logo-spin infinite 20s linear; + } +} + +.card { + padding: 2em; +} + +.read-the-docs { + color: #888; +} diff --git a/src/App.tsx b/src/App.tsx new file mode 100644 index 0000000..c98d30b --- /dev/null +++ b/src/App.tsx @@ -0,0 +1,17 @@ +import { Routes, Route } from 'react-router' +import Home from './pages/Home' + +export default function App() { + return ( + + } /> + } /> + } /> + } /> + } /> + } /> + } /> + } /> + + ) +} diff --git a/src/components/ui/accordion.tsx b/src/components/ui/accordion.tsx new file mode 100644 index 0000000..d21b65f --- /dev/null +++ b/src/components/ui/accordion.tsx @@ -0,0 +1,64 @@ +import * as React from "react" +import * as AccordionPrimitive from "@radix-ui/react-accordion" +import { ChevronDownIcon } from "lucide-react" + +import { cn } from "@/lib/utils" + +function Accordion({ + ...props +}: React.ComponentProps) { + return +} + +function AccordionItem({ + className, + ...props +}: React.ComponentProps) { + return ( + + ) +} + +function AccordionTrigger({ + className, + children, + ...props +}: React.ComponentProps) { + return ( + + svg]:rotate-180", + className + )} + {...props} + > + {children} + + + + ) +} + +function AccordionContent({ + className, + children, + ...props +}: React.ComponentProps) { + return ( + +
{children}
+
+ ) +} + +export { Accordion, AccordionItem, AccordionTrigger, AccordionContent } diff --git a/src/components/ui/alert-dialog.tsx b/src/components/ui/alert-dialog.tsx new file mode 100644 index 0000000..935eecf --- /dev/null +++ b/src/components/ui/alert-dialog.tsx @@ -0,0 +1,155 @@ +import * as React from "react" +import * as AlertDialogPrimitive from "@radix-ui/react-alert-dialog" + +import { cn } from "@/lib/utils" +import { buttonVariants } from "@/components/ui/button" + +function AlertDialog({ + ...props +}: React.ComponentProps) { + return +} + +function AlertDialogTrigger({ + ...props +}: React.ComponentProps) { + return ( + + ) +} + +function AlertDialogPortal({ + ...props +}: React.ComponentProps) { + return ( + + ) +} + +function AlertDialogOverlay({ + className, + ...props +}: React.ComponentProps) { + return ( + + ) +} + +function AlertDialogContent({ + className, + ...props +}: React.ComponentProps) { + return ( + + + + + ) +} + +function AlertDialogHeader({ + className, + ...props +}: React.ComponentProps<"div">) { + return ( +
+ ) +} + +function AlertDialogFooter({ + className, + ...props +}: React.ComponentProps<"div">) { + return ( +
+ ) +} + +function AlertDialogTitle({ + className, + ...props +}: React.ComponentProps) { + return ( + + ) +} + +function AlertDialogDescription({ + className, + ...props +}: React.ComponentProps) { + return ( + + ) +} + +function AlertDialogAction({ + className, + ...props +}: React.ComponentProps) { + return ( + + ) +} + +function AlertDialogCancel({ + className, + ...props +}: React.ComponentProps) { + return ( + + ) +} + +export { + AlertDialog, + AlertDialogPortal, + AlertDialogOverlay, + AlertDialogTrigger, + AlertDialogContent, + AlertDialogHeader, + AlertDialogFooter, + AlertDialogTitle, + AlertDialogDescription, + AlertDialogAction, + AlertDialogCancel, +} diff --git a/src/components/ui/alert.tsx b/src/components/ui/alert.tsx new file mode 100644 index 0000000..1421354 --- /dev/null +++ b/src/components/ui/alert.tsx @@ -0,0 +1,66 @@ +import * as React from "react" +import { cva, type VariantProps } from "class-variance-authority" + +import { cn } from "@/lib/utils" + +const alertVariants = cva( + "relative w-full rounded-lg border px-4 py-3 text-sm grid has-[>svg]:grid-cols-[calc(var(--spacing)*4)_1fr] grid-cols-[0_1fr] has-[>svg]:gap-x-3 gap-y-0.5 items-start [&>svg]:size-4 [&>svg]:translate-y-0.5 [&>svg]:text-current", + { + variants: { + variant: { + default: "bg-card text-card-foreground", + destructive: + "text-destructive bg-card [&>svg]:text-current *:data-[slot=alert-description]:text-destructive/90", + }, + }, + defaultVariants: { + variant: "default", + }, + } +) + +function Alert({ + className, + variant, + ...props +}: React.ComponentProps<"div"> & VariantProps) { + return ( +
+ ) +} + +function AlertTitle({ className, ...props }: React.ComponentProps<"div">) { + return ( +
+ ) +} + +function AlertDescription({ + className, + ...props +}: React.ComponentProps<"div">) { + return ( +
+ ) +} + +export { Alert, AlertTitle, AlertDescription } diff --git a/src/components/ui/aspect-ratio.tsx b/src/components/ui/aspect-ratio.tsx new file mode 100644 index 0000000..3df3fd0 --- /dev/null +++ b/src/components/ui/aspect-ratio.tsx @@ -0,0 +1,11 @@ +"use client" + +import * as AspectRatioPrimitive from "@radix-ui/react-aspect-ratio" + +function AspectRatio({ + ...props +}: React.ComponentProps) { + return +} + +export { AspectRatio } diff --git a/src/components/ui/avatar.tsx b/src/components/ui/avatar.tsx new file mode 100644 index 0000000..b7224f0 --- /dev/null +++ b/src/components/ui/avatar.tsx @@ -0,0 +1,51 @@ +import * as React from "react" +import * as AvatarPrimitive from "@radix-ui/react-avatar" + +import { cn } from "@/lib/utils" + +function Avatar({ + className, + ...props +}: React.ComponentProps) { + return ( + + ) +} + +function AvatarImage({ + className, + ...props +}: React.ComponentProps) { + return ( + + ) +} + +function AvatarFallback({ + className, + ...props +}: React.ComponentProps) { + return ( + + ) +} + +export { Avatar, AvatarImage, AvatarFallback } diff --git a/src/components/ui/badge.tsx b/src/components/ui/badge.tsx new file mode 100644 index 0000000..fd3a406 --- /dev/null +++ b/src/components/ui/badge.tsx @@ -0,0 +1,46 @@ +import * as React from "react" +import { Slot } from "@radix-ui/react-slot" +import { cva, type VariantProps } from "class-variance-authority" + +import { cn } from "@/lib/utils" + +const badgeVariants = cva( + "inline-flex items-center justify-center rounded-full border px-2 py-0.5 text-xs font-medium w-fit whitespace-nowrap shrink-0 [&>svg]:size-3 gap-1 [&>svg]:pointer-events-none focus-visible:border-ring focus-visible:ring-ring/50 focus-visible:ring-[3px] aria-invalid:ring-destructive/20 dark:aria-invalid:ring-destructive/40 aria-invalid:border-destructive transition-[color,box-shadow] overflow-hidden", + { + variants: { + variant: { + default: + "border-transparent bg-primary text-primary-foreground [a&]:hover:bg-primary/90", + secondary: + "border-transparent bg-secondary text-secondary-foreground [a&]:hover:bg-secondary/90", + destructive: + "border-transparent bg-destructive text-white [a&]:hover:bg-destructive/90 focus-visible:ring-destructive/20 dark:focus-visible:ring-destructive/40 dark:bg-destructive/60", + outline: + "text-foreground [a&]:hover:bg-accent [a&]:hover:text-accent-foreground", + }, + }, + defaultVariants: { + variant: "default", + }, + } +) + +function Badge({ + className, + variant, + asChild = false, + ...props +}: React.ComponentProps<"span"> & + VariantProps & { asChild?: boolean }) { + const Comp = asChild ? Slot : "span" + + return ( + + ) +} + +export { Badge, badgeVariants } diff --git a/src/components/ui/breadcrumb.tsx b/src/components/ui/breadcrumb.tsx new file mode 100644 index 0000000..eb88f32 --- /dev/null +++ b/src/components/ui/breadcrumb.tsx @@ -0,0 +1,109 @@ +import * as React from "react" +import { Slot } from "@radix-ui/react-slot" +import { ChevronRight, MoreHorizontal } from "lucide-react" + +import { cn } from "@/lib/utils" + +function Breadcrumb({ ...props }: React.ComponentProps<"nav">) { + return