Spoken (TTS) replies are 1-2 sentences, so an unbounded num_predict only
exposes the worst case where the chat model rambles or loops. Add an
ollama_num_predict config (default 512, 0 disables) wired into the reply
loop's chat call on both the native- and text-tool paths. The 512-token
headroom stays well above this app's short tool-call JSON, so capping never
truncates a tool call. This keeps the user's quality model instead of
downgrading it. Configurable in the container via OLLAMA_NUM_PREDICT.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
docker/jarvis-config.template.json hardcoded "tts_engine": "piper". entrypoint
renders it into /app/config/jarvis.json, and bridge _tts_engine_setting() reads
that JSON BEFORE the env — so TTS_ENGINE=melo in .env was ignored and the bot
synthesised Korean with the English Piper voice (the "foreign accent" the user
heard); the warm melo-worker sat unused.
Template now carries ${TTS_ENGINE}; compose sets TTS_ENGINE=${TTS_ENGINE:-melo}
so envsubst renders the real engine. Verified: envsubst with TTS_ENGINE=melo
yields "tts_engine": "melo", and `docker compose config` passes TTS_ENGINE=melo.
Added a regression test that the template stays env-driven and renders the
configured engine.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
The config template never set intent_judge_model, so it fell back to the code
default gemma4:e2b. That model is not pulled by this stack (Ollama only has
qwen2.5:3b, qwen3:8b, nomic-embed-text), so every auxiliary small-model call —
intent judge, tool router, weather place extraction, query decomposition —
targeted a non-existent model, silently failed, and fell open. This crippled
tool routing and argument extraction on the 3B brain.
Render intent_judge_model from a new OLLAMA_INTENT_MODEL env var that defaults
to OLLAMA_CHAT_MODEL, so the auxiliary calls reuse the already-warm chat model
(one resident model, no extra load). tool_router_model="" then resolves through
the chain to the same model.
Verified: rendered jarvis.json now has intent_judge_model=qwen2.5:3b, and the
weather place extractor returns "서울" / "Tokyo" (it returned None for
everything while pointed at the missing gemma4:e2b).
`docker compose up -d --build` now brings up the whole thing automatically —
no host setup needed:
- All-in-one javis image: TigerVNC+XFCE desktop, Chrome, Python brain bridge,
Node/bun bot, managed by supervisord (verified: all 6 programs RUNNING).
- ollama service + one-shot ollama-init that auto-pulls chat+embed models
(verified end-to-end; `ollama list` shows pulled models).
- Discord token deferred: without DISCORD_BOT_TOKEN the desktop, bridge,
Ollama and models all run; only the bot waits (no crash loop).
- Slim container deps (bridge/requirements-bridge.txt) drop the unused
PyQt6/torch/chatterbox/sounddevice stack. Piper voice + Whisper models
auto-download into named volumes.
- Configurable host ports (VNC_PORT/NOVNC_PORT/BRIDGE_PORT) to avoid clashing
with a host VNC already on 5901. Bridge binds 0.0.0.0 in-container.
Verified: image builds; brain imports; bridge /health 200; noVNC 200;
X display :1 @1920x1080; auto-pull completes; supervisorctl status all RUNNING.
