2 Commits

Author	SHA1	Message	Date
Jeff Emmett	2fe77055a9	feat(meeting-intelligence): add per-meeting access control ... Restrict MI data so only meeting attendees can access their meetings. Each meeting gets a unique access_token generated at creation time. Attendees discover their token via conference_id (room name) lookup. Backend: - New auth.py with Bearer token validation and multi-token extraction - Token generation in create_meeting(), backfill on startup - All endpoints gated: list_meetings filters by X-MI-Tokens header, per-meeting endpoints require Authorization: Bearer <token> - New GET /meetings/token?conference_id=<room> discovery endpoint Frontend: - tokenStorage.ts manages tokens in localStorage keyed by room name - middleware.ts auto-fetches token on CONFERENCE_JOINED - All API calls in actions.ts now include auth headers Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-04-09 10:18:13 -04:00
Jeff Emmett	4cb219db0f	feat(meeting-intelligence): add backend infrastructure for transcription and AI summaries ... Add complete Meeting Intelligence System infrastructure: Backend Services: - PostgreSQL schema with pgvector for semantic search - Transcription service using whisper.cpp and resemblyzer for diarization - Meeting Intelligence API with FastAPI - Jibri configuration for recording API Endpoints: - /meetings - List, get, delete meetings - /meetings/{id}/transcript - Get transcripts with speaker attribution - /meetings/{id}/summary - Generate AI summaries via Ollama - /search - Full-text and semantic search - /meetings/{id}/export - Export as PDF, Markdown, JSON - /webhooks/recording-complete - Jibri callback Features: - Zero-cost local transcription (whisper.cpp CPU) - Speaker diarization (who said what) - AI-powered summaries with key points, action items, decisions - Vector embeddings for semantic search - Multi-format export Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>	2026-02-05 19:04:19 +00:00