- Add logger.audit/access calls to auth route (login, login_failed,
login_blocked, register, password_changed, 2fa_backup_code_used,
2fa_backup_codes_regenerated) — these previously only wrote to DB
via raw SQL, bypassing Loki shipper
- Replace logger.info with logger.apiCall in callAI() so every AI call
ships to Loki with model, tokens, cost, duration
- Add device identifier (parsed user agent) to audit and access logs
- Fix TTS voice/model provider mismatch: auto-detect Vertex voices
(Puck, Charon, Kore, etc.) and ElevenLabs voice IDs, override model
to match provider regardless of what model was previously set
- Fix TTS discovery: model IDs saved to tts.voice are detected and
redirected to tts.model (regex for openai-tts, elevenlabs, vertex-tts)
- Fix STT transcription route: add scribe/elevenlabs/transcri to the
isTranscriptionModel regex so ElevenLabs Scribe uses /audio/transcriptions
endpoint instead of chat completions
- Remove OpenObserve/SigNoz code from logger (reverted to Loki-only)
- Add neonatal assessment calculator: GA classification (extremely preterm through
post term), weight-for-GA percentile (AGA/SGA/LGA) using Fenton 2013 LMS data,
birth weight category (ELBW/VLBW/LBW/normal/macrosomia)
- Add DOCX support via mammoth, PPTX/ODT/EPUB via jszip in Learning Hub content
generator file upload
- Add gatewayUrl() helper for consistent API URL construction — handles
LITELLM_API_BASE with or without /v1 suffix, works with any OpenAI-compatible
gateway (LiteLLM, Bifrost, etc.)
- Fix TTS model/voice separation: discovery now tags items as MODEL or VOICE,
auto-detects provider from voice name (Vertex, ElevenLabs, OpenAI)
- Fix STT discovery to include ElevenLabs Scribe and Chirp models
- Fix TTS discovery to include ElevenLabs and Vertex voices alongside models
- Fix admin model test to bypass allowlist check (skipAllowlistCheck) so
discovered models can be tested before adding
- Fix Nextcloud token decryption in learningAI.js WebDAV browse and file import
- Fix admin embedding test to show DB model name instead of hardcoded default
- Fix admin STT test to use correct endpoint for Whisper models
- Add AI gateway migration guide to configuration docs
- Add Grafana dashboard JSON for Loki log visualization
Security:
- Add session management: users can view/revoke active sessions in Settings
- Add password change in Settings (requires current password, HIBP check)
- Force logout all sessions on password reset
- Fix logout to destroy server-side session (was only clearing cookie)
- Add trust proxy for correct client IP in rate limiting and audit logs
- Add CORS support for multiple domains (CORS_ORIGINS env var)
- Add HIBP breach check endpoint and inline warnings on password fields
Audit logging:
- Add audit logging to all 24 PHI-handling endpoints across 13 route files
- Covers: generation, transcription, TTS, refine, encounters, documents, Nextcloud
- All fire-and-forget (no response delay)
AI improvements:
- Refine now includes original source material (transcript, notes, labs)
so AI can reference the full input when modifying output
- Add correction tracking (trackAIOutput) to sick visit and well visit tabs
- Fix sickvisit missing from encounter save noteIdMap
UI fixes:
- Non-blocking busy bar for transcription and AI generation (replaces full-screen overlay)
- Fix encounter recording: hide record button during recording (was showing two stop buttons)
- Fix ROS/PE "All WNL" stacking duplicate event handlers; add Clear buttons
- Enlarge AI instructions textarea in Learning Hub CMS
Domain:
- Primary domain now app.pedshub.com, with scribe.pedshub.com and peds.danvics.com as CORS origins
- 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
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).
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
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.
- TRANSCRIBE_PROVIDER=litellm routes audio to LiteLLM /audio/transcriptions
- TTS_PROVIDER=litellm routes to LiteLLM /audio/speech
- Both auto-detect when LITELLM_API_BASE is set (no extra config needed)
- LITELLM_STT_MODEL (default: whisper-1), LITELLM_TTS_MODEL (default: tts-1)
- LITELLM_TTS_VOICE (default: alloy) — alloy/echo/fable/onyx/nova/shimmer
- ElevenLabs still works if ELEVENLABS_API_KEY is set and TTS_PROVIDER=elevenlabs
- Health endpoint now reports tts provider
- Add GET /api/transcribe/status endpoint — returns whether any server
transcription provider (Whisper/AWS/Local) is configured
- Frontend checks status on login via checkTranscribeStatus()
- When no provider configured: recording stops instantly, keeps live
Web Speech API text, shows friendly toast — no error, no upload wait
- Works in encounter, dictation, and SOAP tabs
- App now works fully out-of-the-box with just an AI provider key
- AWS Transcribe: remove 10ms delay between chunks (was adding ~1.25s/MB),
increase chunk size from 8KB to 32KB (AWS max per frame)
- Add detailed timing logs (ffmpeg, streaming, total) for diagnostics
- OpenAI Whisper: use response_format='text' for faster response parsing
- Frontend: show transcription time in toast, request 16kHz sample rate,
increase bitrate to 32kbps Opus (better quality, still small files)
- Return duration in API response for all providers
- Add local Whisper (whisper.cpp / faster-whisper) as transcription provider
Set TRANSCRIBE_PROVIDER=local with configurable model size and binary path
- Upgrade all refine/instruction inputs to resizable textareas across
encounter, dictation, hospital course, chart review, well visit, sick visit
- Make AI memory injection flexible: physician preferences and corrections
are now actively applied (not just "formatting reference"), while still
overridable by current prompt instructions
- New src/utils/transcribeAWS.js: streams audio directly to AWS
Transcribe without requiring an S3 bucket
- Supports AWS_TRANSCRIBE_MEDICAL=true for Transcribe Medical
(better clinical accuracy: drug names, diagnoses, procedures)
- AWS_TRANSCRIBE_SPECIALTY configures specialty (default PRIMARYCARE)
- transcribe.js auto-selects AWS when AWS_BEDROCK_REGION is set,
or can be forced with TRANSCRIBE_PROVIDER=aws|openai
- Falls back to OpenAI Whisper when AWS is not configured
- Add @aws-sdk/client-transcribe-streaming as optional dependency
- Update .env.example with transcription configuration docs
