- New docs/developer-guide.md: full walkthrough of frontend SPA architecture,
backend middleware stack, database layer, AI integration, settings system,
how to add features/routes/tables, key design decisions, file references
- Expand ai-providers.md: detailed admin model management (add custom models
with ID/name/cost/category, discover from provider, enable/disable, set default)
- Update README docs index
- Add Cloudflare Turnstile to login, register, and password reset forms
- Switch AI provider to LiteLLM, transcription to OpenAI Whisper
- Change domain to scribe.pedshub.com
- Fix PPTX export: add tables, bold/italic, numbered lists, code blocks, blockquotes
- Fix announcement banner close button (CSP was blocking inline onclick)
- Fix auth middleware: empty Bearer token now falls through to cookie auth
- Fix audio backups: only save on transcription failure, stop auto-deleting on success
- Soften AI correction injection to prevent model hallucination from correction history
- Fix LiteLLM TTS model name handling (no incorrect openai/ prefix)
- Expand AI instructions textarea in Learning Hub CMS
- Update README for v6 with all features and providers
- Add comprehensive docs/: architecture, API reference, database schema,
authentication, AI providers, speech, learning hub, configuration, deployment
- Fix model search for all providers: Bedrock now falls back to built-in
list (with live ListFoundationModels attempt), Azure returns built-in list
- Add Test button on every model row (built-in, discovered, custom) that
sends a live prompt and shows response + latency in a toast
- Add TTS management section: search voices from provider API (Google TTS
voices.list, LiteLLM /v1/models, ElevenLabs /v1/voices), Set as Default
writes tts.voice/tts.model to DB, runtime respects DB override
- Add STT management section: search models from provider (Gemini, Whisper,
LiteLLM, OpenAI, local), Set as Default writes stt.model to DB, runtime
respects DB override in transcribe.js
- Add Embedding models section: search from provider (LiteLLM, Vertex,
OpenAI), Set as Default writes embeddings.model+dimensions to DB,
embeddings.js respects DB override
- Add record-and-transcribe STT test (browser MediaRecorder)
- Add TTS synthesize-and-play test (returns base64 audio)
- Add embedding generate test (shows dims + vector sample)
- Expand PUT /config/:key(*) whitelist to include tts., stt., embeddings.
- Add @aws-sdk/client-bedrock as optional dependency for live Bedrock discovery
REMOVED:
- Milestone editing UI from Admin Panel (per user request)
- Milestones will be managed via hardcoded static data only
- Kept backend routes and database support for future use
ADDED:
- Refresh button in Learning Hub CMS content list
- Manual refresh for AI-generated content updates
- Better discoverability of content refresh functionality
FIXES:
- AI learning content now has visible refresh button
- Users can manually refresh content list after AI generation
- Cleaner admin panel without milestone management clutter
NOTE:
- Developmental milestones still work via static fallback
- Edit milestones by modifying public/js/milestonesData.js
- Backend API still supports milestone management if needed later
NEW FEATURES:
- Bulk Import button in Admin Panel → Developmental Milestones section
- "Import Default Milestones Data" button appears when database is empty
- "Re-import All" button to clear and re-import all static data
- Visible notice when no milestones exist with one-click import
IMPROVEMENTS:
- Auto-shows empty state notice when database has no milestones
- Backend bulk-import endpoint now supports clearExisting parameter
- Imports ALL age groups from static data (birth to 11 years)
- Better UX - admin doesn't need CLI to populate milestone data
FIXES:
- Makes milestone admin editing feature discoverable and usable
- No need to manually run import script anymore
FIXES:
- Milestones now show correctly on encounter page (use static fallback if DB empty)
- Static data preserved as MILESTONES_DATA_STATIC for compatibility
- Database-driven milestones still work (admin can edit via CMS)
NEW FEATURES:
- OpenID Connect (OIDC) authentication support (PocketID, Keycloak, Azure AD, etc.)
- Comprehensive setup guide: OPENID_SETUP.md
- Auto-linking existing users by email on SSO login
- Multiple PDF upload support in Learning Hub (up to 10 files)
- 100 MB per file limit (was 20 MB)
- Full PDF content used for AI generation
- Embeddings use first ~8K chars for semantic search
IMPROVEMENTS:
- Updated UI to show multiple file selection with list
- Drag-and-drop supports multiple files
- Better file upload validation and error handling
- Added clarifying comments about embedding truncation
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.
Provides two transcription options:
1. Browser Whisper (Offline, Batch) - RECOMMENDED
- 100% offline, zero network calls
- HIPAA-compliant, audio never leaves device
- Highest accuracy (Whisper)
- Processes after recording (batch mode)
- Models self-hosted, bundled in v2
2. Web Speech API (Real-time, Streaming) - EXPERIMENTAL
- Real-time transcription (see words as you speak)
- Uses browser's built-in speech recognition
- ⚠️ Sends audio to cloud (Chrome/Edge → Google)
- ⚠️ NOT HIPAA-compliant
- Requires user consent with clear warnings
Features:
- Settings UI for both options
- Clear privacy warnings for Web Speech
- Mutual exclusion (only one active at a time)
- Browser detection shows which provider is used
- Confirmation dialog before enabling Web Speech
Use Cases:
- Clinical/HIPAA: Use Browser Whisper only
- Personal/Non-clinical: Can use Web Speech for real-time feedback
- Maximum privacy: Browser Whisper (offline)
- Maximum speed: Web Speech (if privacy not required)
Implementation:
- speechRecognition.js: Web Speech API wrapper
- transcriptionSettings.js: Settings UI handler
- Privacy info displayed per browser
User can choose based on their privacy vs. speed preference.
Features:
- Admin can add, edit, and delete developmental milestones via dashboard
- Milestones stored in PostgreSQL (developmental_milestones table)
- Client-side loads milestones from API instead of static file
- Import script to migrate existing static data to database
- Organized by age group and domain
- Supports sorting and filtering
Admin UI:
- New section in Admin panel for milestone management
- Filter by age group
- Add/Edit modal with validation
- Delete with confirmation
- Auto-complete for age groups and domains
API Endpoints:
- GET /api/milestones-data - Public endpoint for authenticated users
- GET /api/admin/milestones - List all milestones (admin only)
- GET /api/admin/milestones/meta - Get age groups and domains
- POST /api/admin/milestones - Create milestone
- PUT /api/admin/milestones/:id - Update milestone
- DELETE /api/admin/milestones/:id - Delete milestone
- POST /api/admin/milestones/bulk-import - Bulk import
Usage:
1. Run import script: node scripts/import-milestones.js
2. Access Admin dashboard → Developmental Milestones section
3. Add/Edit/Delete milestones as needed
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
REALITY CHECK: Browser Whisper CDN loading cannot work in all environments
- Corporate firewalls block cdn.jsdelivr.net
- Network proxies filter JavaScript CDN
- Workers + importScripts + cross-origin = blocked by CSP/CORS
SOLUTION: Graceful degradation
- Clear user-friendly error messages
- Automatic fallback to server transcription
- Warning banner in Settings if CDN blocked
- Comprehensive troubleshooting documentation
Changes:
- browserWhisper.js: Show toast on worker error, fallback gracefully
- app.js: Display CSP warning banner on preload failure
- settings.html: Add warning about network/firewall requirements
- BROWSER_WHISPER_TROUBLESHOOTING.md: Complete guide for users
Key Message:
Browser Whisper is OPTIONAL. Server transcription (Google/AWS/OpenAI)
is the primary method and works everywhere. Browser Whisper is a
privacy-focused bonus feature that requires CDN access.
User Experience:
- If CDN works: Great! Browser Whisper available
- If CDN blocked: No problem! Server transcription works perfectly
- Clear messaging: User knows what to expect
- Allow empty voice value to preview server default
- Display 'server default' in preview text
- Clears user preference (sets to null) when testing default
ROOT CAUSES FOUND AND FIXED:
1. TTS Preview not working: voicePreferences.js listening for wrong event
- Was: 'tab-loaded' (never dispatched)
- Now: 'tabChanged' (correct event name used by app.js)
- Added immediate init if page already loaded
- Added 500ms delay for DOM readiness
2. Browser Whisper CDN blocked: CSP too restrictive
- Added 'unsafe-eval' to scriptSrc (required by transformers.js)
- Added cdn.jsdelivr.net to connectSrc (worker importScripts)
- Added childSrc directive for worker script loading
- Better error messages in worker
3. Worker loading errors: Now logged with specific reasons
- importScripts wrapped in try-catch
- Posts error message to main thread
- Verifies transformers object exists after load
Testing:
- TTS Preview should now work when clicking Settings tab
- Browser Whisper should load from CDN (or show specific error)
- Console logs will show exact init sequence
The button was always returning null because it searched for onclick=speakText
but all output cards use data-action="speak" data-target="id". Now checks
data-action first so the button correctly toggles to Stop during playback.
Emails: white card, clean typography, dark button, no gradients.
Same minimal aesthetic as Linear/Resend/Notion emails.
Verify page responses also updated to match.
LiteLLM /audio/transcriptions gives 'Unmapped provider' for Vertex AI Chirp.
The correct approach: use /v1/chat/completions with a Gemini model and send
audio as base64 input_audio content block — Gemini natively understands audio.
Set LITELLM_STT_MODEL to your Gemini model name (e.g. gemini-2.5-flash).
- Raise express.json limit from 1MB to 10MB — handles large chart reviews
with many notes (50 full clinic notes ≈ 600KB, well within new limit)
- Client-side: warn user if payload >8MB, friendly toast if >30 notes
- Bump to v13.0.0
- Bug 1: When user selected "Outpatient" review type but had any subspecialty
visit cards filled in, the backend ignored the top-level type and switched to
the subspecialty prompt. Fixed: top-level type dropdown is now definitive.
Per-visit note types only control data formatting/labeling, not prompt selection.
- Bug 2: Labs entered in a visit card were silently dropped for outpatient and
subspecialty visits (only ED visit labs were included). Fixed: per-visit labs
now appear immediately after their visit content, labeled with the visit date.
- Improved lab labeling: visit labs are labeled "Labs from this visit (date)"
and the separate labs section is labeled "ADDITIONAL LABS (not tied to a
specific visit)" so the AI clearly distinguishes them.
- TTS response now includes X-TTS-Provider header (google-tts, litellm/model, elevenlabs)
- Frontend reads header and shows actual provider in toast instead of hardcoded "Adam/ElevenLabs"
- CORS exposes X-TTS-Provider header so frontend can access it
- Updated .env.example: clarify that LITELLM_TTS_MODEL and LITELLM_STT_MODEL
can be either the model_name alias OR the full provider/model path depending
on your LiteLLM config (important for BAA compliance routing)
vertex_ai/chirp does not work via LiteLLM's audio transcription proxy.
Changed default LITELLM_STT_MODEL from vertex_ai/chirp to whisper-1.
Updated .env.example documentation to match.
STT: Vertex AI Chirp not supported via LiteLLM proxy (confirmed by docs).
Now uses Gemini directly (transcribeGoogle.js) — auto-detected when
GOOGLE_VERTEX_PROJECT is set, fallback to AWS then OpenAI.
TTS: LiteLLM Vertex TTS DOES work but requires the model_list ALIAS
(tts-1) not the underlying path (vertex_ai/text-to-speech).
Also pass voice param — LiteLLM supports Google Cloud voice names.
Auto-detected when LITELLM_API_BASE is set.
LiteLLM's atranscription has a routing bug with Vertex AI Chirp proxy.
AWS Transcribe is already configured and working. Auto-detect now prefers
AWS over LiteLLM. Use TRANSCRIBE_PROVIDER=litellm to force LiteLLM.
- TTS: switch to axios, drop voice param (configured in LiteLLM per model)
- STT: log full LiteLLM error body so 500s are diagnosable in logs
- TTS: same error detail logging
- Fix 'ElevenLabs unavailable' toast to generic 'TTS unavailable'
- Add red Stop button to encounter recording UI
- TTS: switch from OpenAI SDK to axios (same fix as STT), drop voice
param since it's configured inside LiteLLM per model
- Fix 'ElevenLabs unavailable' toast shown even when provider is LiteLLM
- Add dedicated red Stop button to encounter recording UI
Vertex AI Chirp via LiteLLM rejects/hangs when 'prompt' and
'response_format' are included — these are OpenAI Whisper-only params.
Send only file + model for LiteLLM/Chirp.
Having LITELLM_API_BASE for AI text was auto-routing audio transcription
through LiteLLM even when the proxy has no Whisper model configured,
causing silent hangs. Now LiteLLM STT only activates when LITELLM_STT_MODEL
is explicitly set. Falls back correctly to AWS Transcribe when configured.
OpenAI SDK's audio.transcriptions.create() hangs with LiteLLM
(no timeout, SDK-level incompatibility with multipart handling).
Use axios + form-data directly with 120s timeout — same approach
as ElevenLabs TTS. Handles both {text:"..."} and plain string responses.
