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
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.
Stakeholders: Enterprise meeting participants, meeting recorders, admin users (ymirliu@panjit.com.tw)
Constraints:
- Must run faster-whisper int8 on i5/8GB laptop
- DB credentials and API keys must stay server-side (security)
- All database tables prefixed with
meeting_ - Output must support Traditional Chinese (繁體中文)
Goals / Non-Goals
Goals:
- Deliver working MVP with all six capabilities
- Secure architecture with secrets in middleware only
- Offline-capable transcription
- Structured output with trackable action items
Non-Goals:
- Multi-language support beyond Traditional Chinese
- Real-time collaborative editing
- Mobile client
- Custom LLM model training
Architecture
┌─────────────────────────────────────────────────────────────────┐
│ Electron Client │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────────────────┐ │
│ │ Auth UI │ │ Meeting UI │ │ Transcription Engine │ │
│ │ (Login) │ │ (CRUD/Edit) │ │ (faster-whisper+OpenCC)│ │
│ └──────┬──────┘ └──────┬──────┘ └────────────┬────────────┘ │
└─────────┼────────────────┼──────────────────────┼───────────────┘
│ │ │
│ HTTP │ HTTP │ Local only
▼ ▼ ▼
┌─────────────────────────────────────────────────────────────────┐
│ FastAPI Middleware Server │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌────────┐ │
│ │ Auth Proxy │ │Meeting CRUD │ │ Dify Proxy │ │ Export │ │
│ │ POST /login │ │POST/GET/... │ │POST /ai/... │ │GET /:id│ │
│ └──────┬──────┘ └──────┬──────┘ └──────┬──────┘ └───┬────┘ │
└─────────┼────────────────┼────────────────┼─────────────┼───────┘
│ │ │ │
▼ ▼ ▼ │
┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
│ PJ-Auth API │ │ MySQL │ │ Dify LLM │ │
│ (Vercel) │ │ (theaken.com)│ │(theaken.com) │ │
└──────────────┘ └──────────────┘ └──────────────┘ │
│
┌────────────────────┘
▼
┌──────────────┐
│ Excel Template│
│ (openpyxl) │
└──────────────┘
Decisions
Decision 1: Three-tier architecture with middleware
Choice: All external services accessed through FastAPI middleware Rationale: Security requirement - DB credentials and API keys cannot be in Electron client Alternatives considered:
- Direct client-to-service: Rejected due to credential exposure risk
- Serverless functions: More complex deployment for similar security
Decision 2: Edge transcription in Electron
Choice: Run faster-whisper locally via Python sidecar (PyInstaller) Rationale: Offline capability requirement; network latency unacceptable for real-time transcription Alternatives considered:
- Cloud STT (Google/Azure): Requires network, latency issues
- WebAssembly whisper: Not mature enough for production
Decision 3: MySQL with prefixed tables
Choice: Use shared MySQL instance with meeting_ prefix
Rationale: Leverage existing infrastructure; prefix ensures isolation
Alternatives considered:
- Dedicated database: Overhead not justified for MVP
- SQLite: Doesn't support multi-user access
Decision 4: Dify for LLM summarization
Choice: Use company Dify instance for AI features Rationale: Already available infrastructure; structured JSON output support Alternatives considered:
- Direct OpenAI API: Additional cost, no existing infrastructure
- Local LLM: Hardware constraints (i5/8GB insufficient)
Risks / Trade-offs
| Risk | Impact | Mitigation |
|---|---|---|
| faster-whisper performance on i5/8GB | High | Use int8 quantization; test on target hardware early |
| Dify timeout on long transcripts | Medium | Implement chunking; add timeout handling with retry |
| Token expiry during long meetings | Medium | Implement auto-refresh interceptor in client |
| Network failure during save | Medium | Client-side queue with retry; local draft storage |
Data Model
-- Tables all prefixed with meeting_
meeting_users (user_id, email, display_name, role, created_at)
meeting_records (meeting_id, uuid, subject, meeting_time, location,
chairperson, recorder, attendees, transcript_blob,
created_by, created_at)
meeting_conclusions (conclusion_id, meeting_id, content, system_code)
meeting_action_items (action_id, meeting_id, content, owner, due_date,
status, system_code)
ID Formats:
- Conclusions:
C-YYYYMMDD-XX(e.g., C-20251210-01) - Action Items:
A-YYYYMMDD-XX(e.g., A-20251210-01)
API Endpoints
| Method | Endpoint | Purpose |
|---|---|---|
| POST | /api/login | Proxy auth to PJ-Auth API |
| GET | /api/meetings | List meetings (filterable) |
| POST | /api/meetings | Create meeting |
| GET | /api/meetings/:id | Get meeting details |
| PUT | /api/meetings/:id | Update meeting |
| DELETE | /api/meetings/:id | Delete meeting |
| POST | /api/ai/summarize | Send transcript to Dify |
| GET | /api/meetings/:id/export | Generate Excel report |
Open Questions
- None currently - PRD and SDD provide sufficient detail for MVP implementation