feat: Meeting Assistant MVP - Complete implementation
Enterprise Meeting Knowledge Management System with: Backend (FastAPI): - Authentication proxy with JWT (pj-auth-api integration) - MySQL database with 4 tables (users, meetings, conclusions, actions) - Meeting CRUD with system code generation (C-YYYYMMDD-XX, A-YYYYMMDD-XX) - Dify LLM integration for AI summarization - Excel export with openpyxl - 20 unit tests (all passing) Client (Electron): - Login page with company auth - Meeting list with create/delete - Meeting detail with real-time transcription - Editable transcript textarea (single block, easy editing) - AI summarization with conclusions/action items - 5-second segment recording (efficient for long meetings) Sidecar (Python): - faster-whisper medium model with int8 quantization - ONNX Runtime VAD (lightweight, ~20MB vs PyTorch ~2GB) - Chinese punctuation processing - OpenCC for Traditional Chinese conversion - Anti-hallucination parameters - Auto-cleanup of temp audio files OpenSpec: - add-meeting-assistant-mvp (47 tasks, archived) - add-realtime-transcription (29 tasks, archived) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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## Context
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Building a meeting knowledge management system for enterprise users. The system must support offline transcription on standard hardware (i5/8GB), integrate with existing company authentication, and provide AI-powered summarization via Dify LLM.
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**Stakeholders**: Enterprise meeting participants, meeting recorders, admin users (ymirliu@panjit.com.tw)
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**Constraints**:
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- Must run faster-whisper int8 on i5/8GB laptop
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- DB credentials and API keys must stay server-side (security)
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- All database tables prefixed with `meeting_`
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- Output must support Traditional Chinese (繁體中文)
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## Goals / Non-Goals
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**Goals**:
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- Deliver working MVP with all six capabilities
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- Secure architecture with secrets in middleware only
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- Offline-capable transcription
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- Structured output with trackable action items
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**Non-Goals**:
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- Multi-language support beyond Traditional Chinese
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- Real-time collaborative editing
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- Mobile client
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- Custom LLM model training
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## Architecture
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```
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┌─────────────────────────────────────────────────────────────────┐
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│ Electron Client │
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│ ┌─────────────┐ ┌─────────────┐ ┌─────────────────────────┐ │
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│ │ Auth UI │ │ Meeting UI │ │ Transcription Engine │ │
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│ │ (Login) │ │ (CRUD/Edit) │ │ (faster-whisper+OpenCC)│ │
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│ └──────┬──────┘ └──────┬──────┘ └────────────┬────────────┘ │
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└─────────┼────────────────┼──────────────────────┼───────────────┘
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│ │ │
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│ HTTP │ HTTP │ Local only
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▼ ▼ ▼
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┌─────────────────────────────────────────────────────────────────┐
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│ FastAPI Middleware Server │
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│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌────────┐ │
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│ │ Auth Proxy │ │Meeting CRUD │ │ Dify Proxy │ │ Export │ │
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│ │ POST /login │ │POST/GET/... │ │POST /ai/... │ │GET /:id│ │
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│ └──────┬──────┘ └──────┬──────┘ └──────┬──────┘ └───┬────┘ │
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└─────────┼────────────────┼────────────────┼─────────────┼───────┘
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│ │ │ │
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▼ ▼ ▼ │
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┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
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│ PJ-Auth API │ │ MySQL │ │ Dify LLM │ │
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│ (Vercel) │ │ (theaken.com)│ │(theaken.com) │ │
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└──────────────┘ └──────────────┘ └──────────────┘ │
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│
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┌────────────────────┘
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▼
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┌──────────────┐
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│ Excel Template│
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│ (openpyxl) │
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└──────────────┘
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```
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## Decisions
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### Decision 1: Three-tier architecture with middleware
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**Choice**: All external services accessed through FastAPI middleware
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**Rationale**: Security requirement - DB credentials and API keys cannot be in Electron client
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**Alternatives considered**:
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- Direct client-to-service: Rejected due to credential exposure risk
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- Serverless functions: More complex deployment for similar security
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### Decision 2: Edge transcription in Electron
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**Choice**: Run faster-whisper locally via Python sidecar (PyInstaller)
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**Rationale**: Offline capability requirement; network latency unacceptable for real-time transcription
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**Alternatives considered**:
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- Cloud STT (Google/Azure): Requires network, latency issues
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- WebAssembly whisper: Not mature enough for production
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### Decision 3: MySQL with prefixed tables
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**Choice**: Use shared MySQL instance with `meeting_` prefix
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**Rationale**: Leverage existing infrastructure; prefix ensures isolation
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**Alternatives considered**:
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- Dedicated database: Overhead not justified for MVP
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- SQLite: Doesn't support multi-user access
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### Decision 4: Dify for LLM summarization
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**Choice**: Use company Dify instance for AI features
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**Rationale**: Already available infrastructure; structured JSON output support
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**Alternatives considered**:
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- Direct OpenAI API: Additional cost, no existing infrastructure
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- Local LLM: Hardware constraints (i5/8GB insufficient)
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## Risks / Trade-offs
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| Risk | Impact | Mitigation |
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|------|--------|------------|
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| faster-whisper performance on i5/8GB | High | Use int8 quantization; test on target hardware early |
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| Dify timeout on long transcripts | Medium | Implement chunking; add timeout handling with retry |
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| Token expiry during long meetings | Medium | Implement auto-refresh interceptor in client |
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| Network failure during save | Medium | Client-side queue with retry; local draft storage |
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## Data Model
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```sql
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-- Tables all prefixed with meeting_
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meeting_users (user_id, email, display_name, role, created_at)
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meeting_records (meeting_id, uuid, subject, meeting_time, location,
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chairperson, recorder, attendees, transcript_blob,
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created_by, created_at)
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meeting_conclusions (conclusion_id, meeting_id, content, system_code)
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meeting_action_items (action_id, meeting_id, content, owner, due_date,
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status, system_code)
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```
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**ID Formats**:
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- Conclusions: `C-YYYYMMDD-XX` (e.g., C-20251210-01)
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- Action Items: `A-YYYYMMDD-XX` (e.g., A-20251210-01)
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## API Endpoints
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| Method | Endpoint | Purpose |
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|--------|----------|---------|
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| POST | /api/login | Proxy auth to PJ-Auth API |
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| GET | /api/meetings | List meetings (filterable) |
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| POST | /api/meetings | Create meeting |
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| GET | /api/meetings/:id | Get meeting details |
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| PUT | /api/meetings/:id | Update meeting |
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| DELETE | /api/meetings/:id | Delete meeting |
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| POST | /api/ai/summarize | Send transcript to Dify |
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| GET | /api/meetings/:id/export | Generate Excel report |
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## Open Questions
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- None currently - PRD and SDD provide sufficient detail for MVP implementation
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