# LLM backend: "local" or "lmstudio"
llm:
  backend: local        # change to "lmstudio" to use LM Studio instead
  max_cache_tokens: 4096  # max KV-cache size per session (tokens); 0 to disable caching

  system_prompt: >-
    You are a helpful voice assistant. 
    Keep your responses extremely concise but natural for spoken conversation.
    Do not use markdown, bullet points, code blocks, emojis, or any formatting that doesn't work in speech.

  # Settings used only when backend = "lmstudio"
  lmstudio:
    url: http://host.docker.internal:1234   # host.docker.internal resolves to your PC from inside Docker
    model: ""           # leave empty to use whatever model LM Studio has loaded

# Avatar video generation (Wan2.2-Lightning fp8 via LightX2V + MuseTalk lip-sync)
video:
  enabled: false                  # master toggle — when false, video models are not loaded
  backend: lightx2v               # only option for now
  mode: reflective                # "library" (pre-baked clips) | "reflective" (fresh per turn)
  resolution: 480                 # 480 or 720
  fps: 16                         # Wan2.2 native rate; MuseTalk resamples as needed

  library:
    base_clip_count: 4            # how many speaking base clips to pre-generate per avatar
    base_clip_seconds: 6          # duration of each pre-baked clip

  reflective:
    clip_seconds: 5               # target length of each fresh Wan2.2 clip per turn
    clip_prompt_template: >-
      webcam view of a person speaking, {reply_hint},
      casual gestures, natural lighting, soft focus background
    prompt_reply_words: 18        # max words lifted from reply to inject as {reply_hint}

  # Model sources for the video stack. The fp8 e4m3 4-step distilled DIT
  # weights from lightx2v/Wan2.2-Distill-Models are ~15 GB each (vs ~28 GB
  # bf16) — that's the "save VRAM" path. T5/VAE/tokenizer still come from
  # the Wan-AI base repo. Both repos download on first run into
  # HF_HOME=/cache/huggingface.
  models:
    wan22_base_repo: Wan-AI/Wan2.2-I2V-A14B
    wan22_fp8_repo: lightx2v/Wan2.2-Distill-Models
    wan22_model_cls: wan2.2_moe_distill
    wan22_config_json: /app/configs/lightx2v/wan22_i2v_fp8_distill.json
    musetalk_path: TMElyralab/MuseTalk

  # LoRAs applied to the fp8 base at load time via runtime switch_lora.
  # Wan2.2 is a MoE with separate high-noise and low-noise sub-models —
  # `target` picks which sub-model each LoRA attaches to. The two files
  # below are the user-supplied ./loras/wan22-[HL]-e8.safetensors mounted
  # into the container at /cache/loras/.
  loras:
    - path: /cache/loras/wan22-H-e8.safetensors
      weight: 1.0
      target: high_noise
      name: wan22-H-e8
    - path: /cache/loras/wan22-L-e8.safetensors
      weight: 1.0
      target: low_noise
      name: wan22-L-e8