# Javis configuration. Copy to config.yaml and edit.

gui:
  theme: "dark"             # dark | light  (Tools → "Toggle light/dark theme")
  show_startup_dialog: true # show the configuration dialog before the main window
  minimize_to_tray: true    # closing the window hides to the tray; quit via File → Quit
  global_ptt_hotkey: "ctrl+alt+space"  # system-wide push-to-talk (Windows); "" = off

llm:
  base_url: "http://localhost:11434"
  model: "gemma3:4b"
  temperature: 0.7
  request_timeout_seconds: 120
  # Keep the model loaded in Ollama for 1 h after each request (default is
  # only 5 min, so gaps > 5 min force a full model reload next turn).
  # -1 keeps it loaded until Ollama restarts.
  keep_alive: 3600

stt:
  model_size: "large-v3"     # tiny, base, small, medium, large-v3, distil-large-v3
  device: "auto"             # auto, cuda, cpu
  compute_type: "float16"    # float16, int8_float16, int8, float32
  language: null             # null = auto-detect
  hotwords: ""               # bias Whisper towards names, e.g. "Javis, Esszimmer, Homey"

tts:
  backend: "piper"           # piper (Piper VITS — CPU ONNX, default) | f5 (F5-TTS voice cloning)
  device: "auto"
  default_voice: "default"   # Piper voice name (see the Voices tab / voice-ref-curator)

# Overnight voice training — the default voice is retrained once per night
# inside the start_hour → end_hour window, resuming from the latest checkpoint.
# Toggle/disable here (or in Settings → Voice Training); changes apply live.
training:
  enabled: true
  start_hour: 3              # window opens at 03:00 (0-23)
  end_hour: 6                # window closes at 06:00 (0-23)
  min_remaining_minutes: 60  # don't start a run if less window time remains
  max_epochs: 6000
  batch_size: 32
  num_workers: 2
  accelerator: "gpu"         # gpu | cpu
  precision: "16-mixed"      # 32 | 16-mixed | bf16-mixed
  language: "de"             # espeak-ng voice code used for phonemisation (de, en-us, …)

audio:
  sample_rate: 16000
  input_device: null         # null = system default; or int device index
  output_device: null
  thinking_cue_delay_s: 2.5  # soft tick while Javis is still working; 0 = off

wake_word:
  enabled: true
  model: "hey_jarvis"        # built-in openWakeWord name, or a custom model: "rufus" -> ~/.javis/models/wakeword/rufus.onnx (see docs/WAKE_WORD.md)
  threshold: 0.5
  vad_threshold: 0.5         # silero-vad probability threshold
  vad_gate: 0.0              # >0: wake only when openWakeWord's own VAD agrees (fewer false wakes from TV/music)
  silence_ms: 800            # end-of-utterance silence
  voice_gate_ratio: 3.0      # how much louder than the background a frame must be to count as
                             # the user talking — lets recording end while your own speakers play
                             # music (Silero rates music as speech). 0 = off. Higher is stricter:
                             # raise it if music keeps the recorder running, lower it if quiet
                             # commands get cut off.

mcp:
  servers_config: "mcp_servers.json"
  approval_default: "ask"    # ask | allow | deny  (headless: "ask" is denied — no UI)
  confirm_destructive: true  # ask before tools marked destructiveHint, even on "allow" servers
  call_timeout_seconds: 30
  approval_timeout_seconds: 45  # auto-deny if the approval dialog isn't answered; 0 = wait
  voice_approval: true       # answer the approval prompt with "ja"/"nein" (needs STT + mic)

api:
  # HTTP API for javis-slave companion devices (plain HTTP — trusted LAN only).
  enabled: false
  host: "0.0.0.0"            # bind address; 0.0.0.0 = all interfaces
  port: 8765
  pairing_code_ttl_seconds: 300   # lifetime of a single-use pairing code

agent:
  # Persona preset. Choose one: default | smart_home | concise | verbose
  preset: "default"
  # Optional verbatim system prompt (overrides the preset above).
  system_prompt_inline: null
  # Optional path to a .txt file (highest priority — overrides everything else).
  system_prompt_file: null
  history_max_turns: 20
  # Maximum LLM <-> tool-call iterations per turn before giving up.
  max_llm_iterations: 5
  # Spoken feedback for voice users (empty string = silent).
  error_phrase: "Entschuldigung, da ist etwas schiefgelaufen. Bitte versuch es noch einmal."
  # Spoken when the GPU was lost (e.g. NVIDIA driver updated while running).
  gpu_lost_phrase: "Die Grafikkarte ist nicht mehr erreichbar, vermutlich wurde der Treiber aktualisiert. Bitte starte Javis neu."
  approval_phrase: "Soll ich das ausführen? Sag ja oder nein."

memory:
  # "ollama" uses llm.base_url; "hermes" uses the remote Hermes API server.
  provider: "ollama"
  # Hermes API root (not the dashboard URL). Required when provider is hermes.
  base_url: null
  # Store API_SERVER_KEY in this environment variable; never put it in YAML.
  api_key_env: "JAVIS_HERMES_API_KEY"
  # Dedicated model for background fact extraction.
  # null = use the same model as llm.model.
  # Set to a smarter or reasoning model for higher-quality extraction without
  # slowing down the main conversation (extraction runs in the background).
  # Examples: "qwen2.5:14b", "qwen3:8b", "deepseek-r1:8b", "llama3.1:8b"
  model: null
  # Temperature for extraction calls. Low values (0.0–0.2) give more consistent
  # JSON output. Reasoning models work well at 0.1.
  temperature: 0.1
  # Context window for extraction. Much smaller than the main LLM is fine —
  # extraction prompts are short. Reduce if memory calls are slow.
  num_ctx: 4096
  # Timeout (seconds) for extraction calls. 120 is a safe default.
  # Increase for reasoning models on slow hardware (deepseek-r1 can take 3–5 min on CPU).
  # Set to null only to inherit llm.request_timeout_seconds (not recommended).
  timeout_seconds: 120
  # Maximum number of new facts to extract per turn.
  max_facts: 2
  # Maximum number of existing memories included in the extraction prompt
  # (most-recently-updated first). Raise for larger memories, lower to keep
  # extraction prompts short.
  max_existing_for_prompt: 25

paths:
  base_dir: null             # null = ~/.javis