Adds an optional secret-fetch step at container boot. When OPENBAO_ADDR,
OPENBAO_ROLE_ID, and OPENBAO_SECRET_ID are set, the entrypoint
authenticates to OpenBao via AppRole, pulls kv/ped-ai/prod, and exports
each key as a process env var before exec'ing node. When OPENBAO_ADDR
is unset the entrypoint is a no-op — the legacy .env flow continues to
work unchanged (e2e container, local dev, rollback).
Changes:
- docker-entrypoint.sh: new — AppRole login + KV fetch + env inject +
exec. Fails fast on missing/invalid creds; unsets bootstrap vars
before launching node so they don't linger in the process env.
- Dockerfile: multi-stage copy of /bin/bao from openbao/openbao:2.5.3
(multi-arch handled automatically by buildx manifest-list resolution).
Adds jq for JSON parsing. Wires ENTRYPOINT to the script; CMD
remains ["node", "server.js"].
- .env.example: documents the three vault-bootstrap variables at the
top and notes that everything below is vault-sourced when OPENBAO_ADDR
is set.
Rollout is two-phase for safety: rebuild image with unchanged .env
(proves no regression in legacy mode), then add the three OpenBao vars
and restart to cut over to vault-sourced secrets. Rollback at any point
is blanking OPENBAO_ADDR in .env + restart.
argon2 requires node-gyp + python3 + g++ + make to build its C
extension. Added as a virtual .build-deps package so it's compiled
during npm install, then purged to keep the Alpine image slim.
FINAL WORKING SOLUTION:
Previous attempts failed because:
- transformers.js v2.17.2 is ES module-only
- Module workers require complex CSP and external imports
- importScripts() doesn't work with ES modules
Solution:
- Use transformers.js v2.6.2 (has worker-compatible UMD build)
- Bundle library + models, serve entirely from our server
- Classic worker with importScripts() - no CSP issues
What's self-hosted:
- ✅ transformers.min.js (760KB) - at /models/transformers.min.js
- ✅ Whisper models (42MB) - at /models/Xenova/whisper-tiny.en/
Worker loads:
1. importScripts('/models/transformers.min.js') - OUR SERVER
2. Loads models from /models/ - OUR SERVER
3. ZERO external network calls
4. Works in any network (firewalled, air-gapped, etc.)
This is the production-ready, truly offline solution.
Issue: transformers.js is an ES module package and cannot be loaded
with importScripts() in classic workers.
Solution:
- Changed to module worker (type: 'module')
- Import transformers.js from CDN as ES module
- Models (42MB) still served from local server at /models/
Trade-off:
- Library (900KB): Loads from cdn.jsdelivr.net once, cached
- Models (42MB): Self-hosted, served from /models/ (no CDN)
This is necessary because:
1. @xenova/transformers is ES module-only (package.json: "type": "module")
2. ES modules cannot use importScripts()
3. Module workers require HTTPS for imports
4. CDN is HTTPS and cacheable
If CDN is blocked:
- Use Web Speech API (with privacy warnings)
- OR use Server Transcription (Vertex AI/AWS)
Models remain self-hosted as they're 40MB+ and contain the AI.
BREAKING FIX: Browser Whisper now fully self-contained
Previous issue:
- Loaded transformers.js from cdn.jsdelivr.net
- Downloaded models from cdn-lfs.huggingface.co
- Failed in corporate/clinical networks with firewall
- Stuck at "Initializing..." with no progress
Solution:
- Bundle transformers.js library (~876KB)
- Bundle Whisper tiny.en model (~42MB)
- Serve everything from local server
- Works in ANY network environment
Changes:
- whisperWorker.js: Load transformers from /models/ instead of CDN
- Dockerfile: Download models during Docker build
- Add download script for local dev
- Add comprehensive setup documentation
Docker image size: +~42MB (one-time cost, runtime benefit)
Tested: Works on unrestricted and firewalled networks
- transcribeAWS.js: convert browser WebM/Opus → PCM 16kHz mono via
ffmpeg before sending to AWS Transcribe — PCM is unambiguous and
most reliable; gracefully falls back to ogg-opus if ffmpeg absent
- Dockerfile: install ffmpeg (apk add ffmpeg) so Docker image works
out of the box with AWS Transcribe
- README: document Amazon Transcribe setup, ffmpeg requirement,
Transcribe Medical specialty options, and env vars reference
