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### 性能优化 - 后端任务执行从串行锁改为 ThreadPoolExecutor 并发执行（默认3线程） - 添加 GZipMiddleware 响应压缩 + Nginx gzip 配置 - 数据库连接池参数优化（pool_size=10, max_overflow=20） - 视频帧提取并行化（ThreadPoolExecutor） - LLM 重试配置缓存到实例，避免每次请求读 env var - 前端路由级代码拆分（React.lazy + Suspense） - Vite manualChunks 拆分 markdown/markmap/vendor - MarkdownViewer 用 React.memo + useMemo 减少不必要渲染 - NoteHistory Fuse.js 实例 useMemo 缓存 - useTaskPolling 无待处理任务时跳过轮询 - 移除 antd 依赖（NoteForm Alert、modelForm Tag），改用 shadcn/ui ### 前端转写器配置（新功能） - 新增 TranscriberConfigManager（JSON 文件存储，替代环境变量） - 新增 GET/POST /transcriber_config API 端点 - 新增 GET /transcriber_models_status 模型下载状态查询 - 新增 POST /transcriber_download 后台模型下载触发 - 前端转写器设置页面：引擎选择、模型大小选择、模型下载管理 - deploy_status 端点同步从配置文件读取 ### Bug 修复 - 修复任务进行中切换页面后进度丢失：Home.tsx status 派生逻辑补全中间状态 - 修复 MLX Whisper 静默回退 fast-whisper：移除环境变量门控，macOS 下自动尝试导入 - MLX Whisper 不可用时抛出 RuntimeError 而非静默回退 - 前端展示 MLX Whisper 可用性状态，不可用时禁用保存 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
75 lines
3.8 KiB
Markdown
75 lines
3.8 KiB
Markdown
# CLAUDE.md
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This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
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## Project Overview
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BiliNote is an AI video note generation tool. It extracts content from video links (Bilibili, YouTube, Douyin, Kuaishou, local files) and generates structured Markdown notes using LLM models. Full-stack app with a FastAPI backend, React frontend, and optional Tauri desktop packaging.
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## Development Commands
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### Backend (Python 3.11 + FastAPI)
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```bash
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cd backend
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pip install -r requirements.txt
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python main.py # Starts on 0.0.0.0:8483
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```
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### Frontend (React 19 + Vite + TypeScript)
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```bash
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cd BillNote_frontend
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pnpm install
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pnpm dev # Dev server on port 3015, proxies /api to backend
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pnpm build # Production build
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pnpm lint # ESLint
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```
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### Docker
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```bash
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docker-compose up # Web stack (backend + frontend + nginx)
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docker-compose -f docker-compose.gpu.yml up # GPU variant
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```
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### Desktop (Tauri)
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```bash
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cd backend && ./build.sh # Build PyInstaller backend binary
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cd BillNote_frontend && pnpm tauri build
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```
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## Architecture
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**Backend** (`backend/`) — FastAPI app, entry point `main.py`:
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- `app/routers/` — API routes: `note.py` (generation), `provider.py`, `model.py`, `config.py`
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- `app/services/` — Business logic: `note.py` (NoteGenerator orchestrates the full pipeline), `task_serial_executor.py` (task queue)
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- `app/downloaders/` — Platform adapters (bilibili, youtube, douyin, kuaishou, local) with shared `base.py` interface
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- `app/transcriber/` — Speech-to-text engines (fast-whisper, groq, bcut, kuaishou, mlx-whisper) with factory in `transcriber_provider.py`
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- `app/gpt/` — LLM integration with factory pattern (`gpt_factory.py`), prompt templates (`prompt.py`, `prompt_builder.py`), and `request_chunker.py` for long transcripts
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- `app/db/` — SQLite + SQLAlchemy: DAO pattern (`provider_dao.py`, `model_dao.py`, `video_task_dao.py`), models in `models/`
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- `app/utils/` — `response.py` (ResponseWrapper for consistent JSON), `video_helper.py` (screenshots via FFmpeg), `export.py` (PDF/DOCX)
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- `events/` (root level) — Blinker signal system for post-processing (e.g., temp file cleanup after transcription)
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**Frontend** (`BillNote_frontend/src/`) — React 19 + Vite + Tailwind + shadcn/ui:
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- `pages/HomePage/` — Main note generation UI: `NoteForm.tsx` (input), `MarkdownViewer.tsx` (preview), `MarkmapComponent.tsx` (mind map)
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- `pages/SettingPage/` — LLM provider management, system monitoring, transcriber config
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- `store/` — Zustand stores: `taskStore`, `modelStore`, `configStore`, `providerStore`
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- `services/` — Axios API clients matching backend routes
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- `hooks/useTaskPolling.ts` — Polls task status every 3 seconds
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- `components/ui/` — shadcn/ui (Radix-based) components
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- Path alias: `@` → `./src`
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**Core Workflow**: User submits URL → task queued → download video → extract audio (FFmpeg) → transcribe (Whisper/Groq/etc) → generate notes (LLM) → frontend polls for completion → display Markdown + mind map.
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## Key Configuration
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- **Ports**: Backend 8483, Frontend dev 3015, Docker maps 3015→80
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- **Environment**: Root `.env` (copy from `.env.example`). LLM API keys are configured through the UI, not env vars.
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- **Database**: SQLite at `backend/app/db/bili_note.db`, auto-initialized on first run
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- **FFmpeg**: Required system dependency for video/audio processing
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- **Vite proxy**: Dev server proxies `/api` and `/static` to backend (configured in `vite.config.ts`, reads env from parent dir)
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## Code Style
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- **Frontend**: ESLint + Prettier (2 spaces, single quotes, 100 char width, Tailwind plugin). TypeScript strict mode.
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- **Backend**: Python with type hints. No configured linter. Uses Pydantic models for validation.
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- **Note**: The frontend directory is named `BillNote_frontend` (not "Bili").
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