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- AILogo: `custom` 名称为合法兜底场景，不再以 console.error 上报；其余未匹配名称降级为 console.warn - SettingPage/Model: 双栏加 `min-h-0 overflow-y-auto`，让供应商列表与右侧表单各自可滚动 - ProviderService.add_provider: API 创建一律落到 `type='custom'`，并对同名供应商抛 ValueError，避免再产生伪内置行 - CLAUDE.md: 补充 v2.0.0 子系统（RAG/Chat、可选 Nacos+RabbitMQ、i18n、cookie/transcriber 管理器） Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
85 lines
5.2 KiB
Markdown
85 lines
5.2 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`, `chat.py` (RAG Q&A on generated notes)
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- `app/services/` — Business logic:
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- `note.py` — `NoteGenerator` orchestrates the full pipeline (download → transcribe → LLM → notes)
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- `task_serial_executor.py` — task queue
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- `chat_service.py` + `chat_tools.py` + `vector_store.py` — RAG-based AI Q&A with Function Calling, indexing transcripts and video metadata
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- `cookie_manager.py` — per-platform cookie storage; injected into yt-dlp by downloaders (e.g. Bilibili)
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- `transcriber_config_manager.py` — persisted transcriber settings
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- `worker_registry.py` — **optional** Nacos registration + heartbeat for distributed worker mode (no-op when `NACOS_SERVER_ADDR` unset)
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- `app/messaging/` — **optional** RabbitMQ producer/consumer publishing task progress/results to `bilinote.task.feedback` exchange. Silently degrades when `RABBITMQ_URL` is unset; always import-safe.
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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`. YouTube path prefers existing subtitles and skips audio download when available.
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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), `ppt_generator.py`, `minio_client.py`
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- `app/i18n/` — backend localization
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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`. Persists to IndexedDB.
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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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- `i18n/` — `react-i18next` setup with locale JSON in `i18n/locales/`; toggled via `components/LanguageSwitcher.tsx`
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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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- **Distributed mode (optional)**: Setting `NACOS_SERVER_ADDR` enables Nacos worker registration; setting `RABBITMQ_URL` enables MQ feedback. Both are no-ops when unset — single-node deployment works without either. Other knobs: `WORKER_ID`, `WORKER_SELF_URL`, `WORKER_MAX_CONCURRENT`, `TASK_MAX_WORKERS`.
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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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