# =============================================================================
# Transcription Connector Configuration
# =============================================================================
#
# Speakr supports multiple transcription providers through a connector-based
# architecture. This file documents all available configuration options.
#
# Quick Start (Simplified):
# 1. For OpenAI with diarization: Set TRANSCRIPTION_MODEL=gpt-4o-transcribe-diarize
# 2. For self-hosted ASR: Set ASR_BASE_URL=http://your-asr:9000
# 3. For legacy Whisper: Set TRANSCRIPTION_API_KEY and optionally TRANSCRIPTION_MODEL
#
# Auto-Detection Priority:
# 1. TRANSCRIPTION_CONNECTOR - explicit connector name (if you need full control)
# 2. ASR_BASE_URL - if set, uses ASR endpoint connector
# 3. TRANSCRIPTION_MODEL contains 'gpt-4o' - uses OpenAI Transcribe connector
# 4. Default - uses OpenAI Whisper connector with TRANSCRIPTION_MODEL or whisper-1

# =============================================================================
# TEXT GENERATION MODEL (REQUIRED for summaries, titles, chat)
# =============================================================================
# Speakr uses a text/LLM model for generating summaries, titles, and chat.
# This is separate from the transcription model (STT).
#
# You can use OpenRouter (recommended - access to many models) or direct OpenAI API.

# OpenRouter example (recommended - supports many models):
TEXT_MODEL_BASE_URL=https://openrouter.ai/api/v1
TEXT_MODEL_API_KEY=your_openrouter_api_key
TEXT_MODEL_NAME=openai/gpt-4o-mini

# OpenAI direct example:
# TEXT_MODEL_BASE_URL=https://api.openai.com/v1
# TEXT_MODEL_API_KEY=sk-your_openai_api_key
# TEXT_MODEL_NAME=gpt-4o-mini

# --- Prefix-Cache-Friendly Prompts (Optional, default: false) ---
# Opt-in. Only useful on self-hosted text backends with automatic prefix
# caching (vLLM APC, llama.cpp/Ollama, SGLang, TGI). When true, the title and
# summary calls share a byte-identical system message + transcript-first prefix
# so the backend prefills the transcript once and reuses it on the second call.
# Leave false for hosted APIs (OpenRouter/OpenAI) or to keep upstream prompts.
# NOTE: the caching must also be enabled on the server itself, e.g. start vLLM
# with --enable-prefix-caching (add --enable-prompt-tokens-details to see the
# cache hits in the admin Token Usage dashboard). See
# docs/admin-guide/model-configuration.md for details.
# PREFIX_CACHE_OPTIMIZED_PROMPTS=false

# --- GPT-5 Specific Settings (only used with OpenAI API and GPT-5 models) ---
# By default GPT-5 parameter handling (reasoning_effort/verbosity,
# max_completion_tokens) activates only for GPT-5 model names on the official
# OpenAI API. Set this to force it on for GPT-5 models served through Azure AI
# Foundry or other gateways the URL check cannot detect:
# GPT5_FORCE_MODE=true
# Same, for a separately configured chat model (not inherited from the above):
# CHAT_GPT5_FORCE_MODE=true
#
# Reasoning effort: minimal, low, medium, high (default: medium)
GPT5_REASONING_EFFORT=medium
# Verbosity: low, medium, high (default: medium)
GPT5_VERBOSITY=medium

# --- Chat Model Configuration (Optional) ---
# Configure a separate model for real-time chat interactions.
# If not set, chat will use the TEXT_MODEL_* settings above.
# CHAT_MODEL_API_KEY=your_chat_api_key
# CHAT_MODEL_BASE_URL=https://openrouter.ai/api/v1
# CHAT_MODEL_NAME=openai/gpt-4o

# =============================================================================
# CONNECTOR SELECTION (Auto-detected if not set)
# =============================================================================
# Options: openai_whisper, openai_transcribe, asr_endpoint, mistral, vibevoice, funasr, mossland, assemblyai, openasr
# Leave empty to auto-detect based on other settings
# TRANSCRIPTION_CONNECTOR=

# Feature flag to enable/disable new connector architecture (default: true)
# Set to false to use legacy code path for troubleshooting
# USE_NEW_TRANSCRIPTION_ARCHITECTURE=true

# =============================================================================
# OPENAI CONFIGURATION (Required for openai_whisper and openai_transcribe)
# =============================================================================
TRANSCRIPTION_API_KEY=your_openai_api_key
TRANSCRIPTION_BASE_URL=https://api.openai.com/v1

# Model Selection - determines which connector is used:
#
# whisper-1              - Legacy Whisper model, no diarization, $0.006/min
#                          Supports: srt, vtt, json, verbose_json output formats
#
# gpt-4o-transcribe      - High quality transcription, no diarization, $0.006/min
#                          Better accuracy than whisper-1, accepts prompts
#
# gpt-4o-mini-transcribe - Cost-effective option, no diarization, $0.003/min
#                          Good for high-volume, budget-conscious use
#
# gpt-4o-transcribe-diarize - Speaker diarization!, $0.006/min
#                             Identifies speakers as A, B, C, D...
#                             Requires chunking_strategy for audio >30s
#
TRANSCRIPTION_MODEL=gpt-4o-transcribe-diarize

# Legacy Whisper model name (used when TRANSCRIPTION_MODEL is not set)
# WHISPER_MODEL=whisper-1

# Comma-separated list of transcription model names offered in the per-upload
# model dropdown. Leave empty to hide the dropdown and always use the model
# configured above. Example: whisper-1,gpt-4o-transcribe,gpt-4o-transcribe-diarize
# TRANSCRIPTION_MODELS_AVAILABLE=

# =============================================================================
# ASR ENDPOINT CONFIGURATION (For self-hosted whisper services)
# =============================================================================
# Note: USE_ASR_ENDPOINT is deprecated. Just set ASR_BASE_URL instead.
# The connector will auto-detect ASR mode when ASR_BASE_URL is set.
# USE_ASR_ENDPOINT=true  # Deprecated - kept for backwards compatibility

# Base URL of your ASR service (required if USE_ASR_ENDPOINT=true)
# Supports: whisper-asr-webservice, WhisperX, and compatible services
# ASR_BASE_URL=http://whisper-asr:9000

# Request timeout in seconds (default: 1800 = 30 minutes)
# Increase for very long audio files
# ASR_TIMEOUT=1800

# Enable speaker diarization (default: true)
# ASR_DIARIZE=true

# Speaker count hints (optional, helps with diarization accuracy)
# ASR_MIN_SPEAKERS=1
# ASR_MAX_SPEAKERS=5

# Return speaker embeddings for speaker identification (WhisperX only)
# Enables automatic speaker matching across recordings
# ASR_RETURN_SPEAKER_EMBEDDINGS=false

# =============================================================================
# MISTRAL / VOXTRAL CONFIGURATION
# =============================================================================
# Mistral's Voxtral model provides cloud-based transcription with built-in
# speaker diarization and language detection. Requires a Mistral API key.
#
# TRANSCRIPTION_CONNECTOR=mistral
# TRANSCRIPTION_API_KEY=your_mistral_api_key
# TRANSCRIPTION_MODEL=voxtral-mini-latest
#
# To use a custom Mistral-compatible endpoint:
# TRANSCRIPTION_BASE_URL=https://api.mistral.ai
#
# Enable app-level chunking for the Mistral connector (default: false).
# MISTRAL_ENABLE_CHUNKING=false

# =============================================================================
# VIBEVOICE CONFIGURATION (Self-hosted via vLLM)
# =============================================================================
# Microsoft's VibeVoice ASR model provides transcription with speaker
# diarization, timestamps, and language detection for 50+ languages. It runs
# on your own hardware via vLLM and handles up to 60 minutes per request.
# Longer files are automatically chunked by the app.
#
# Requirements:
# - vLLM server with the VibeVoice model loaded
# - GPU(s) with enough VRAM (bf16 needs ~18GB, fits on 2x consumer GPUs)
#
# TRANSCRIPTION_CONNECTOR=vibevoice
# TRANSCRIPTION_BASE_URL=http://your-vllm-server:8000
# TRANSCRIPTION_MODEL=vibevoice
#
# If your vLLM server requires authentication:
# TRANSCRIPTION_API_KEY=your_api_key

# =============================================================================
# FUNASR CONFIGURATION (Alibaba Cloud)
# =============================================================================
# Alibaba Cloud FunASR (DAMO Academy speech recognition) provides high-accuracy
# transcription with speaker diarization at very low cost. Requires S3-compatible
# storage for passing pre-signed audio URLs to the API.
#
# Docs: https://help.aliyun.com/zh/model-studio/fun-asr-recorded-speech-recognition-restful-api
#
# TRANSCRIPTION_CONNECTOR=funasr
# ASR_BASE_URL=https://dashscope.aliyuncs.com/api/v1
# ASR_API_KEY=sk-your-aliyun-key
# ASR_MODEL=paraformer-v2       # 'paraformer-v2' (low cost) or 'fun-asr'
# ASR_TIMEOUT=1800              # Transcription timeout in seconds (default 30min)
# ASR_DIARIZE=false             # Speaker diarization (default false)
# ASR_LANGUAGE_HINTS=zh,en      # Language hints, comma-separated (default zh,en)
#
# --- S3 Storage (Required for FunASR) ---
# FunASR requires audio files to be available through pre-signed S3 URLs.
# S3_ENDPOINT_URL is the endpoint Speakr uses for storage and for signed URLs by
# default. Speakr creates signed URLs per transcription task and does not persist
# them in the database.
# S3_BUCKET_NAME=your-bucket
# S3_ENDPOINT_URL=https://oss-cn-shanghai.aliyuncs.com
# Optional: use an Aliyun internal endpoint only for URLs sent to DashScope.
# The URL is signed directly for this host, so do not use a bucket-qualified URL.
# S3_INTRANET_ENDPOINT_URL=https://oss-cn-shanghai-internal.aliyuncs.com
# S3_REGION=cn-shanghai
# S3_ACCESS_KEY_ID=your-access-key-id
# S3_SECRET_ACCESS_KEY=your-secret-access-key
# --- FunASR-specific parameters (FUNASR_* prefix) ---
# FUNASR_POLL_INTERVAL=10          # Seconds between status polls (default 10s)
# FUNASR_DISFLUENCY_REMOVAL=false  # Filter filler words (default false)
# FUNASR_TIMESTAMP_ALIGNMENT=false # Timestamp alignment (default false)
# FUNASR_SPEAKER_COUNT=2           # Speaker count hint, 2-100
# FUNASR_CHANNEL_ID=0              # Audio channel indices, comma-separated
# FUNASR_VOCABULARY_ID=            # Hot-word vocabulary ID

# =============================================================================
# MOSI / MOSSLAND MOSS CONFIGURATION (Hosted Cloud API)
# =============================================================================
# MOSS provides single-speaker transcription and multi-speaker diarization.
# Diarized audio stays in one provider task so speaker identities remain stable;
# Speakr app-level chunking is disabled for this connector. The connector uses
# SSE first, then resumes a stalled SSE task through the documented task API.
# It sends one fixed 131072-token output ceiling for long-form transcription.
#
# Privacy: recordings are uploaded to the third-party MOSI/Mossland service at
# api.mosi.cn. They are not processed entirely on your self-hosted Speakr host.
# Create an API key at https://studio.mosi.cn/app/api-keys.
#
# TRANSCRIPTION_CONNECTOR=mossland
# TRANSCRIPTION_API_KEY=your_mosi_api_key
# TRANSCRIPTION_MODEL=moss-transcribe-diarize
# Available transcription models are read on demand from GET /v1/models; the
# two documented model IDs remain available if model discovery is offline.
#
# Optional API-compatible proxy or alternate endpoint:
# TRANSCRIPTION_BASE_URL=https://api.mosi.cn

# =============================================================================
# ASSEMBLYAI CONFIGURATION (Cloud)
# =============================================================================
# AssemblyAI is a cloud transcription provider with strong speaker diarization
# on long, multi-speaker recordings. It is NOT OpenAI-compatible (it uses its
# own async upload/poll API), so it has its own connector. It ingests files up
# to 10 hours in a single job, so the app does not chunk for it.
#
# New accounts get free credits and no credit card is required to get a key
# (sign up at https://www.assemblyai.com and create an API key).
#
# TRANSCRIPTION_CONNECTOR=assemblyai
# TRANSCRIPTION_API_KEY=your_assemblyai_api_key
#
# The endpoint is fixed at https://api.assemblyai.com, so the shared
# TRANSCRIPTION_BASE_URL is intentionally ignored for this connector (a leftover
# value from another connector won't misroute uploads). Set ASSEMBLYAI_BASE_URL
# only if you front the API with a proxy.
# ASSEMBLYAI_BASE_URL=
#
# Optional: pin a speech model (omit to use your account default). Uses a
# dedicated var, NOT the shared TRANSCRIPTION_MODEL (a leftover model name from
# another connector would be rejected by AssemblyAI). Comma-separated values
# become an ordered fallback list (AssemblyAI's speech_models).
# ASSEMBLYAI_SPEECH_MODEL=universal-3-pro,universal-2
#
# Diarization, language, and hotwords come from each user's settings (or tag /
# folder defaults). Hotwords are sent as AssemblyAI word_boost; min/max speaker
# counts map to speaker_options. Times are returned in milliseconds and stored
# as seconds.

# =============================================================================
# CHUNKING CONFIGURATION (For large files)
# =============================================================================
# Chunking is now connector-aware with this priority:
# 1. Connector handles internally (openai_transcribe, asr_endpoint, mistral) → No app chunking
# 2. ENABLE_CHUNKING=false → Disable chunking (only affects openai_whisper)
# 3. CHUNK_LIMIT set → Use your settings
# 4. Connector defaults → Use connector's recommended limits
# 5. App default → 20MB size-based
#
# For openai_transcribe/asr_endpoint/mistral: These settings are IGNORED (connector handles it)
# For openai_whisper: These settings control chunking behavior
# For vibevoice: App chunks files >58 min into ~50 min pieces automatically
# For mossland: App chunking is disabled to preserve speaker identity across long audio

# ENABLE_CHUNKING=false  # Uncomment to disable chunking for openai_whisper

# Chunk limit - supports size (20MB) or duration (600s, 10m)
CHUNK_LIMIT=20MB

# Overlap between chunks in seconds (helps with transcription accuracy at boundaries)
CHUNK_OVERLAP_SECONDS=3

# =============================================================================
# EXAMPLE CONFIGURATIONS (Simplified)
# =============================================================================
#
# --- OpenAI with Speaker Diarization (Recommended) ---
# Just two environment variables needed:
# TRANSCRIPTION_API_KEY=sk-xxx
# TRANSCRIPTION_MODEL=gpt-4o-transcribe-diarize
#
# --- Self-hosted WhisperX (Best for privacy) ---
# Just one environment variable needed (auto-detects ASR mode):
# ASR_BASE_URL=http://whisper-asr:9000
# Optional:
# ASR_DIARIZE=true
# ASR_RETURN_SPEAKER_EMBEDDINGS=true
#
# --- OpenAI Whisper (Legacy, no diarization) ---
# TRANSCRIPTION_API_KEY=sk-xxx
# TRANSCRIPTION_MODEL=whisper-1
#
# --- Custom Whisper model (local or compatible endpoint) ---
# TRANSCRIPTION_API_KEY=not-needed
# TRANSCRIPTION_BASE_URL=http://localhost:8080/v1
# TRANSCRIPTION_MODEL=Systran/faster-distil-whisper-large-v3
#
# --- Mistral Voxtral (cloud diarization) ---
# TRANSCRIPTION_CONNECTOR=mistral
# TRANSCRIPTION_API_KEY=your-mistral-key
# TRANSCRIPTION_MODEL=voxtral-mini-latest
#
# --- VibeVoice ASR (self-hosted diarization) ---
# TRANSCRIPTION_CONNECTOR=vibevoice
# TRANSCRIPTION_BASE_URL=http://your-vllm-server:8000
# TRANSCRIPTION_MODEL=vibevoice
#
# --- MOSI/Mossland MOSS (hosted diarization) ---
# TRANSCRIPTION_CONNECTOR=mossland
# TRANSCRIPTION_API_KEY=your_mosi_api_key
# TRANSCRIPTION_MODEL=moss-transcribe-diarize

# =============================================================================
# APPLICATION SETTINGS
# =============================================================================

# --- Admin User (created on first run) ---
ADMIN_USERNAME=admin
ADMIN_EMAIL=admin@example.com
ADMIN_PASSWORD=changeme

# --- Registration & Access ---
ALLOW_REGISTRATION=false
# Comma-separated list of allowed email domains for registration.
# Leave empty to allow all domains. Example: company.com,subsidiary.org
REGISTRATION_ALLOWED_DOMAINS=

# --- Session Security ---
# Flask secret key: signs session cookies and email/password-reset tokens. It
# does NOT encrypt stored data, so a lost key means a one-time re-login for
# everyone, never data loss. If unset, the app auto-generates a strong key and
# persists it under the instance directory (captured by a normal data-volume
# backup); the old publicly-known built-in default is refused at startup. Set
# this explicitly for multi-host deployments or to control key rotation:
# `python -c "import secrets; print(secrets.token_hex(32))"`.
# SECRET_KEY=change-me-to-a-long-random-string

# Only send the session cookie over HTTPS. STRONGLY recommended (true) for any
# HTTPS deployment. Default: false so plain-HTTP LAN setups keep working.
# SESSION_COOKIE_SECURE=false

# --- Security headers ---
# The app sets X-Frame-Options, X-Content-Type-Options, Referrer-Policy,
# Permissions-Policy, a Content-Security-Policy, and (over HTTPS) HSTS, so a
# deployment without a hardening proxy is still protected. Set
# SECURITY_HEADERS_ENABLED=false only if your proxy already sets all of these.
# Override the CSP wholesale with CONTENT_SECURITY_POLICY, or tune HSTS with
# HSTS_HEADER (empty disables it, e.g. if your proxy already sends HSTS).
# SECURITY_HEADERS_ENABLED=true
# CONTENT_SECURITY_POLICY=
# HSTS_HEADER=max-age=63072000; includeSubDomains

# Number of trusted reverse-proxy hops in front of the app (X-Forwarded-For/
# Proto). Default: 1 (one proxy, e.g. nginx/Traefik). Increase if you chain
# additional proxies/load balancers so client IPs and HTTPS detection are read
# from the correct forwarded header.
# TRUSTED_PROXY_HOPS=1

# --- Token Limits ---
SUMMARY_MAX_TOKENS=8000
CHAT_MAX_TOKENS=5000
# Max tokens for auto title generation (default: 5000)
# TITLE_MAX_TOKENS=5000
# Max tokens for event extraction (default: 3000)
# EVENT_MAX_TOKENS=3000

# --- Logging ---
LOG_LEVEL="INFO"

# =============================================================================
# AUDIO PROCESSING
# =============================================================================

# --- Audio Compression ---
# Automatically compress lossless uploads (WAV, AIFF) to save storage
AUDIO_COMPRESS_UPLOADS=true

# Target codec: mp3 (lossy, smallest), flac (lossless), opus (lossy, efficient)
AUDIO_CODEC=mp3

# Bitrate for lossy codecs (ignored for FLAC)
AUDIO_BITRATE=128k

# Unsupported codecs - comma-separated list of codecs to exclude
# Example: AUDIO_UNSUPPORTED_CODECS=opus,vorbis
# AUDIO_UNSUPPORTED_CODECS=

# =============================================================================
# OPTIONAL FEATURES
# =============================================================================

# --- Help Center Link (optional) ---
# Adds a link to your own help/FAQ/tutorial page in the user menu.
# Label defaults to "Help Center" (localized) if not set.
# HELP_CENTER_URL=https://intranet.example.com/speakr-help
# HELP_CENTER_LABEL=Company FAQ

# --- Inquire Mode (AI search across all recordings) ---
ENABLE_INQUIRE_MODE=false

# Agentic Inquire (opt-in beta). Replaces the single-shot Inquire pipeline
# with a tool-using agent that can search, list and read your recordings
# iteratively before answering, with live activity shown in the UI.
# Requires ENABLE_INQUIRE_MODE=true. Falls back to the classic pipeline on
# any agent failure.
# ENABLE_INQUIRE_AGENT=false
# Tool-calling mode: auto (probe native, fall back to prompted JSON),
# native, or prompt. Default: auto.
# INQUIRE_AGENT_TOOL_MODE=auto
# Loop limits (defaults shown): max tool steps per answer, per-tool-result
# token cap, wall-clock cap, and the conversation context budget that
# triggers history compaction.
# INQUIRE_AGENT_MAX_STEPS=8
# INQUIRE_AGENT_TOOL_RESULT_TOKENS=4000
# INQUIRE_AGENT_TIMEOUT_SECONDS=120
# INQUIRE_CONTEXT_BUDGET_TOKENS=24000
# Defaults for the per-user content availability toggles (users override in
# Account settings; transcripts are always available):
# INQUIRE_DEFAULT_ALLOW_SUMMARIES=true
# INQUIRE_DEFAULT_ALLOW_NOTES=false

# Embedding model for Inquire-mode semantic search.
# Default is a local sentence-transformers model (no API needed).
# EMBEDDING_MODEL=all-MiniLM-L6-v2

# To use an OpenAI-compatible embeddings API instead of the local model,
# set the endpoint and API key below. When EMBEDDING_BASE_URL is set,
# Inquire embeddings are generated via the API instead of locally.
# EMBEDDING_BASE_URL=
#
# Chunking for search indexing. Diarized transcripts are chunked
# segment-aware ("Speaker: text" lines, whole turns kept together, speaker
# and timestamps stored per chunk). Set CHUNKING_STRATEGY=legacy to keep the
# old plain 500-character windows instead. Existing chunks are unaffected
# either way until a recording is reindexed.
# CHUNKING_STRATEGY=segment
# CHUNK_TARGET_CHARS=1400
#
# Embedding API robustness: inputs per request (providers cap this), and how
# long to skip a failing embeddings endpoint before probing again (searches
# fall back to keyword matching in the meantime).
# EMBEDDING_API_BATCH_SIZE=96
# EMBEDDING_API_COOLDOWN_SECONDS=120
# EMBEDDING_API_KEY=

# Optional fixed embedding dimensionality. Leave empty to auto-detect from
# the model/endpoint.
# EMBEDDING_DIMENSIONS=

# --- Automated File Processing (Black Hole Directory) ---
ENABLE_AUTO_PROCESSING=false
# AUTO_PROCESS_MODE=admin_only
# AUTO_PROCESS_WATCH_DIR=/data/auto-process

# --- Automated Export ---
ENABLE_AUTO_EXPORT=false
# AUTO_EXPORT_DIR=/data/exports

# --- Auto-Deletion & Retention ---
ENABLE_AUTO_DELETION=false
# GLOBAL_RETENTION_DAYS=90
# DELETION_MODE=audio_only

# --- Sharing Settings ---
ENABLE_INTERNAL_SHARING=false
ENABLE_PUBLIC_SHARING=true
# SHOW_USERNAMES_IN_UI=false

# --- Permission Controls ---
USERS_CAN_DELETE=true

# Delete speaker profiles when all their recordings are removed.
# Default: false (speaker profiles and voice embeddings are preserved)
# Set to true for privacy-sensitive deployments where biometric voice data
# should not outlive the recordings it was derived from.
# DELETE_ORPHANED_SPEAKERS=false

# --- Video Retention ---
# When enabled, uploaded video files keep their video stream for in-browser playback
# The audio is extracted to a temp file for transcription, then cleaned up
# Also enables the in-app recorder's "Also record video" option for system-audio
# and mic+system recordings (captures the shared tab/window/screen as video)
# Default: false (video uploads extract audio only, video stream is discarded)
VIDEO_RETENTION=false

# Video bitrate cap (kbps) for in-app tab/window/screen video capture.
# Screen content compresses well; 2500 kbps keeps an hour of capture near 1 GB.
# RECORDING_VIDEO_KBPS=2500

# --- Video Passthrough to ASR ---
# Send original video files directly to ASR without extracting audio.
# Useful for custom ASR backends that handle video internally (e.g., multi-track audio extraction).
# When enabled, video files bypass audio extraction, codec conversion, and chunking.
# Only affects video files — audio uploads are processed normally.
# Default: false
# VIDEO_PASSTHROUGH_ASR=false

# --- Concurrent Uploads ---
# Maximum number of simultaneous file uploads (default: 3)
MAX_CONCURRENT_UPLOADS=3

# --- Reliability / Security (optional) ---
# Hard wall-clock cap (seconds) on any single ffmpeg/ffprobe invocation, so a
# malformed or adversarial media file can't hang a processing worker forever.
# Default: 3600 (1 hour).
# FFMPEG_TIMEOUT_SECONDS=3600
#
# Per-endpoint rate limiting on auth routes (login, register, password reset).
# Set to false only if a reverse proxy in front of Speakr already rate-limits
# these. Default: true.
# RATELIMIT_ENABLED=true

# =============================================================================
# SERVER-SIDE RECORDING SESSIONS (optional)
# =============================================================================
# Opt-in: in-app recordings stream audio chunks to the server as they record,
# instead of buffering the entire recording in browser RAM. This enables
# hours-long recordings and resume-on-reload if the tab is closed or refreshed.
# Default: false (recordings are buffered in the browser).
# ENABLE_SERVER_RECORDING_CHUNKS=false

# Absolute ceiling (in hours) on a single in-app recording when server
# chunking is enabled.
# RECORDING_MAX_HOURS=8

# Per-user soft storage cap (in bytes) for in-progress server recording
# sessions. Default: 5368709120 (5 GB).
# RECORDING_SESSION_MAX_BYTES_PER_USER=5368709120
#
# MediaRecorder timeslice (seconds): how often each in-app recording emits
# and uploads a chunk. Smaller = finer crash recovery; larger = fewer
# requests/files/DB writes. Default: 5 (range 1-60).
# RECORDING_CHUNK_SECONDS=5
#
# How many chunks to accumulate before committing session bookkeeping to the
# DB. Chunk files always hit disk immediately; only the row counters lag, and
# finalize re-derives the true count from disk. Raising this cuts DB write
# pressure at scale (important on SQLite, which is single-writer). Default: 1
# (commit every chunk). For many concurrent recorders, consider 6 (~30s at 5s
# chunks) and/or move to PostgreSQL.
# RECORDING_SESSION_COMMIT_BATCH_SIZE=1
#
# Inactivity TTL (hours) before an abandoned in-progress session is reaped.
# Abandoned sessions that have audio are auto-finalized into the user's
# library (not discarded), so a crashed or closed-tab recording is not lost.
# Default: 24.
# RECORDING_SESSION_TTL_HOURS=24

# =============================================================================
# WEBHOOKS (optional)
# =============================================================================
# Outbound webhooks notify external services when events occur (e.g. a
# recording finishes processing). Configured per-user in the UI.

# Master kill-switch for outbound webhook delivery. Set to false to disable
# all webhook delivery globally. Default: true.
# WEBHOOK_GLOBAL_ENABLED=true

# Maximum number of webhook subscriptions allowed per user (default: 10).
# WEBHOOK_MAX_PER_USER=10

# =============================================================================
# BACKGROUND PROCESSING
# =============================================================================

# Transcription queue workers (default: 2)
JOB_QUEUE_WORKERS=2

# Summary queue workers (default: 2)
SUMMARY_QUEUE_WORKERS=2

# Maximum retry attempts for failed jobs (default: 3)
JOB_MAX_RETRIES=3

# =============================================================================
# DOCKER/DATABASE SETTINGS
# =============================================================================

# Database URI - SQLite (default) or PostgreSQL
SQLALCHEMY_DATABASE_URI=sqlite:////data/instance/transcriptions.db
# For PostgreSQL: postgresql://username:password@hostname:5432/database_name

UPLOAD_FOLDER=/data/uploads

# =============================================================================
# FILE STORAGE (Local / S3-Compatible: AWS S3, MinIO)
# =============================================================================

# Storage backend for recording audio files: local | s3
FILE_STORAGE_BACKEND=local

# Key prefix inside the selected backend
FILE_STORAGE_KEY_PREFIX=recordings

# Optional local staging directory for uploads/conversion before final store
# FILE_STORAGE_STAGING_DIR=/data/uploads/_staging

# S3-compatible settings (used when FILE_STORAGE_BACKEND=s3)
# S3_BUCKET_NAME=speakr-audio
# S3_REGION=us-east-1
# S3_ENDPOINT_URL=http://minio:9000
# S3_ACCESS_KEY_ID=minioadmin
# S3_SECRET_ACCESS_KEY=minioadmin
# S3_SESSION_TOKEN=
# S3_USE_PATH_STYLE=true
# S3_VERIFY_SSL=false
# S3_PRESIGN_TTL_SECONDS=900
# S3_PRESIGN_PUBLIC_TTL_SECONDS=300
# Audio delivery for S3 uses redirect to presigned URLs by default.

# =============================================================================
# OPENASR CONFIGURATION (Local server)
# =============================================================================
# OpenASR (https://openasr.org) is an open-source (Apache-2.0), local-first
# speech-to-text desktop app that exposes a local OpenAI-compatible API.
# Audio is processed entirely on your
# machine. Supports diarization, word timestamps, and many model families.
#
# TRANSCRIPTION_CONNECTOR=openasr
# TRANSCRIPTION_BASE_URL=http://127.0.0.1:8080
# TRANSCRIPTION_MODEL=qwen3-asr-0.6b
# TRANSCRIPTION_API_KEY=            # optional; loopback servers can skip auth
#
# Diarization default and request timeout reuse the shared ASR settings:
# ASR_DIARIZE=true
# ASR_TIMEOUT=1800
#
# Per-user hotwords and initial prompts are combined into the standard
# OpenAI-compatible 'prompt' parameter. The server handles long files itself,
# so the app does not chunk for it.

# =============================================================================
# FUTURE: Additional Provider Notes
# =============================================================================
# The connector architecture is designed to support additional providers.
# Currently available: openai_whisper, openai_transcribe, asr_endpoint,
#                      azure_openai_transcribe, mistral, vibevoice
#
# Future connectors may include:
#
# - Deepgram: Known for excellent diarization and real-time transcription
# - AssemblyAI: Strong diarization with speaker labels
# - Google Cloud Speech-to-Text: Enterprise-grade with speaker diarization
#
# To request a new connector, please open an issue on GitHub.