Intent judging, tool routing and arg extraction are classification/JSON calls,
not the spoken answer, yet the stack aliased OLLAMA_INTENT_MODEL back to the big
chat model — so each command paid the big model's cost 2-3 times for routing
before the reply even ran. With the GPU on, that round-trip stacking is the main
remaining per-turn latency. Default OLLAMA_INTENT_MODEL to qwen2.5:3b (the
project's reference small model, clean Korean on classification) and have
ollama-init pull it. The reply engine already routes these calls through
intent_judge_model, so answer quality is untouched; set OLLAMA_INTENT_MODEL =
OLLAMA_CHAT_MODEL to fold back onto one resident model.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
The user chose Microsoft Edge TTS, voice ko-KR-HyunsuMultilingualNeural at rate
+45% (~1.45x), as the natural Korean voice. Wire it into the bridge and make it
the default engine.
- bridge/server.py: _edge_synthesize() calls edge-tts and transcodes the MP3 to
PCM16 mono WAV with the system ffmpeg (temp file for a correct header);
TTS_ENGINE default -> edge; EDGE_TTS_VOICE / EDGE_TTS_RATE env-driven
- requirements-bridge.txt: add edge-tts (lightweight; httpx)
- compose/.env.example/README: TTS_ENGINE=edge + EDGE_TTS_* knobs; note the
online/privacy trade-off (reply text is sent to Microsoft, needs internet)
- drop the now-unused MeloTTS build layer (Dockerfile) and melo-worker
(supervisord) — edge synthesises in-process, no model/worker baked, slimmer
and faster image; settings UI engine list -> edge/piper, restart only bridge
Verified on host: edge-tts -> ffmpeg yields a valid 16-bit mono 24kHz WAV;
envsubst renders tts_engine=edge; docker build --check + 26 tests pass.
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>
MeloTTS's single Korean speaker sounded non-native ("foreign accent"). Swap it
for Coqui XTTS-v2 with the built-in female studio speaker "Ana Florence"
(language ko), the natural voice used in earlier local runs.
- bridge/xtts_worker.py: new warm HTTP worker (own /opt/xtts venv), same
/synth + /health contract and PCM16 output as the old melo worker
- docker/setup-xtts.sh: builds the venv with cu128 torch (Blackwell) + Coqui
TTS and bakes the XTTS-v2 model offline. Pins transformers>=4.57,<5 (5.x
removed isin_mps_friendly, breaking XTTS) and installs the [codec] extra
(torch>=2.9 needs torchcodec) — both verified by a real host synth
- Dockerfile: replace the melo build layer with the xtts layer
- supervisord.conf: melo-worker -> xtts-worker, env passthrough for
XTTS_DEVICE/SPEAKER/LANGUAGE (always set via compose defaults)
- bridge/server.py: default TTS_ENGINE=xtts, route to the xtts worker, generic
worker-synth helper, neural-only fallback flag (XTTS_FALLBACK_PIPER)
- settings UI: engine dropdown xtts/piper, drop the dead melo_speed field, fix
the supervisorctl restart target to xtts-worker
- compose/.env.example/README: XTTS_* vars, speaker/language knobs, remove melo
- remove bridge/melo_worker.py and docker/setup-melo.sh
- tests: xtts treated as multilingual (not English-only)
Verified on host: coqui-tts loads XTTS-v2 and synthesises Korean as
"Ana Florence" to a 16-bit mono 24kHz WAV.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
supervisord.conf passed MELO_DEVICE through as %(ENV_MELO_DEVICE)s but pinned
MELO_SPEED="1.5", so lowering MELO_SPEED in .env had no effect — the worker
always got 1.5. Pass MELO_SPEED through with %(ENV_MELO_SPEED)s and set a
compose default (MELO_SPEED=${MELO_SPEED:-1.5}, same pattern as MELO_DEVICE) so
the supervisord expansion always resolves and an .env value actually changes
the speaking rate. Default rate is unchanged (1.5). melo_worker logs the
resolved speed at startup, so the env->worker path is verifiable.
Verified: _resolve_speed() returns 1.1 for MELO_SPEED=1.1 (1.5 otherwise), and
`MELO_SPEED=1.1 docker compose config` renders MELO_SPEED: "1.1" into the env.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Drop Markdown files into an agents/ folder and their contents are appended to
the main reply LLM's system prompt, so an operator can extend the assistant's
rules/tone without code changes. Files are concatenated in filename order
(use 00-, 10- prefixes to control ordering) and re-read once per turn, so edits
apply on the next reply with no rebuild/restart. Fail-open: a missing, empty,
or unreadable folder yields no instructions and never breaks a reply.
- load_agent_instructions() in system_prompt.py (AGENTS_DIR env, default
/app/agents); reads *.md only, skips blanks, ignores non-dir paths
- engine.py appends it alongside the existing settings-UI llm_instructions,
under the same "Additional instructions from the operator:" framing
- docker-compose.yml bind-mounts ./agents:/app/agents:ro and sets AGENTS_DIR
- agents/example.md.sample starter template (.sample is not loaded)
- tests cover ordering, md-only filtering, blank-skip, env/arg resolution,
and fail-open paths
- README, .env.example, docs/llm_contexts.md updated
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
OAuth cannot be done interactively in the headless container, so the login
must be seeded into the mounted ~/.gemini. Three problems are fixed:
- Mount fragility on the Windows Docker Desktop target: the creds mount
defaulted to ${HOME}/.config/javis/gemini, but ${HOME} is often unset when
compose runs outside a WSL shell, silently mounting the wrong dir. Default is
now the project-local ./docker/gemini-oauth (cross-platform), GEMINI_OAUTH_DIR
still overrides.
- No visibility: when oauth is selected but no login is seeded, the path
silently degraded to DDG/Brave. Added gemini_oauth_ready() + a one-time debug
hint and a startup entrypoint warning (skipped on the browser role, fail-open).
- Seeding guidance: oauth_creds.json is the essential credential (refresh token;
GOOGLE_GENAI_USE_GCA=true forces OAuth), which is what the readiness check and
warning verify; docs recommend copying the whole ~/.gemini for convenience.
Adds docker/gemini-oauth/ seed dir (.gitkeep) with the login files gitignored,
GEMINI_OAUTH_DIR in .env.example, and updates DEPLOY.md, stream_browser_modes.md
and llm_contexts.md. Covered by 3 new tests (10 passed total).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Adds /settings (served by the bridge) to change the LLM model (from installed
Ollama models), Whisper model, TTS engine + MeloTTS speed, output language,
agentic max-turns, thinking mode, and free-form LLM instructions — live, with a
'apply' that restarts the bridge + TTS worker. Settings persist to the runtime
config JSON; engine reads output_language + llm_instructions and the TTS worker
reads melo_speed from it. Bridge port publishable for access.
Base compose is GPU-agnostic; GPU is added by a per-OS override selected via
COMPOSE_FILE in .env (docker-compose.gpu-linux.yml for Ubuntu/CDI,
docker-compose.gpu-windows.yml for Windows 11 Docker Desktop). Adds .env.example
split-deployment section + docs/DEPLOY.md covering all-in-one and browser+bot
layouts on both OSes.
- control-server.mjs runs chrome-control.mjs LOCALLY on the browser host, so a
remote bot's controlBrowser (BROWSER_CONTROL_URL) drives real xdotool input
on THIS screen instead of the bot machine. Published on the LAN.
- controlBrowser tool posts to BROWSER_CONTROL_URL when set, else runs locally.
- Drop hard depends_on ollama so a browser-host doesn't start Ollama.
- gitignore .env.bak*/*.bak (a backup with tokens had been left untracked).
Add JARVIS_ROLE (full|browser|bot) via a run-if-role.sh supervisord guard so
one image serves three layouts. Make Chrome CDP bind configurable (CDP_BIND)
and publishable on the LAN (CDP_PUBLISH_BIND) so a bot on another PC can drive
this host's on-screen Chrome over the internal network (no auth, as requested).
- Site-specified search ("네이버에서 X 검색해줘") now runs controlBrowser.search
directly in the engine when broadcasting, instead of relying on the 3B model
to emit the tool call (it kept narrating "검색하겠습니다" without acting).
- Set OUTPUT_LANGUAGE=ko so replies are Korean-only — stops the small model
leaking CJK/Hanja and English fragments (每, 朗, "feels like") into weather
and other answers, and keeps them concise.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Greetings/small-talk routed no data tool yet still ran the episodic memory
enrichment (LLM keyword extract + diary/graph search, ~1s) every turn. Skip it
when the router picked no external-data tool — the always-injected warm profile
still personalises the reply. Also drop the voice silence-detection wait
800ms -> 600ms for snappier turn-taking. Warm "안녕" now lands well under the
3-4s target.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
CPU MeloTTS serialised under concurrent load (whisper STT + bot) and blew
voice-reply TTS to 7-8s. Install the Blackwell-verified cu128 torch in the
melo venv, select the GPU via MELO_DEVICE=cuda, and do a throwaway synth at
worker startup so the one-off CUDA kernel-init (~5s) doesn't land on the
user's first reply. Measured: ~0.3s/sentence on GPU vs ~1.2-2.6s on CPU.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Whisper 'small' frequently mis-transcribes Korean (esp. the wake word 자비스:
'아비스', '자 비싸'). STT is only ~0.1s warm so there is ample latency headroom;
'medium' is far more accurate and fits VRAM alongside qwen2.5:3b on the 8GB GPU.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Warm per-turn timing showed STT 0.1s, TTS ~1-3s, but the reply engine (LLM) was
8-17s — even a simple "고마워" took 16.7s — because it makes multiple model calls
per turn. Add a PLANNER_ENABLED env override (config.py) and default it to 0 in
the userbot compose so the pre-loop planner's extra LLM round-trip is dropped on
this latency-sensitive voice deployment. Also pins STT_LANGUAGE=ko in compose.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Add an optional OUTPUT_LANGUAGE env var that forces every reply into a
single language. When set, output_language_directive() injects a "respond
only in <language>" instruction (also forbidding other scripts) into the
chat loop's system prompt, next to the existing TTS English-only lock.
Empty (default) keeps the multilingual "reply in the user's language"
behaviour, so upstream is unaffected.
For the Korean-only deployment this also suppresses the occasional trailing
CJK/Hanja fragment qwen2.5:3b leaks on free-form chit-chat.
- system_prompt.py: language-agnostic output_language_directive() helper
- engine.py: read OUTPUT_LANGUAGE, append directive in _build_initial_system_message
- docker-compose.yml + .env.example: document/pass the new var
- docs/llm_contexts.md: note the new gating on the main reply context
- tests: cover unset/set/agnostic/whitespace cases
Replace the blunt global OLLAMA_KEEP_ALIVE=-1 (which kept every model,
including nomic-embed, resident in VRAM forever) with per-request residency:
- llm.py: all three /api/chat payloads send keep_alive=30m so the actively
used chat model stays resident and voice turns never pay a cold reload.
- embeddings.py: /api/embeddings sends keep_alive=0 so nomic-embed unloads
right after each call instead of squatting in VRAM next to the chat model.
- docker-compose.yml: drop the global OLLAMA_KEEP_ALIVE=-1; document the
per-request scheme on the ollama service.
Switch the default chat model qwen3:8b -> qwen2.5:3b. Verified live on the
RTX 5050 (8GB):
- ollama ps: qwen2.5:3b 2.4GB, 100% GPU (8B was 92% GPU / 8% CPU), UNTIL ~30m
(the 30m pin, not "Forever"); nomic-embed absent after several enriched turns.
- nvidia-smi: ~3.2GB VRAM used total (qwen 2.4GB + whisper 0.7GB) vs ~6.6GB.
- Korean /text turns: warm 1.7-4s (cold first load ~52s), vs ~5-7s on 8B;
time/weather/places tool calls fire and reply in Korean.
Known limitation: qwen2.5:3b can occasionally leak a trailing CJK phrase on
free-form chit-chat (factual/tool replies stay clean).
Default ollama keep_alive is 5 min, so any voice turn after a short idle paid
a full Qwen3 8B reload into the GPU (~30-60s) before replying. Set
OLLAMA_KEEP_ALIVE=-1 so the chat + embed models stay loaded.
Measured: post-idle turn ~60s -> steady-state ~4.5s per turn (model pinned
"Forever" in `ollama ps`).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Addresses review findings on the dockerized stack:
- Container Chrome search was dead: add --remote-debugging-port + a non-default
--user-data-dir (Chrome 136+ refuses CDP on the default profile), add the
playwright dep (browse-search.mjs connectOverCDP) with browser download
skipped, and connect to 127.0.0.1 not "localhost" (container localhost -> ::1
while Chrome binds IPv4). Verified: browse-search returns real results.
- Broadcast toggle reliability: always offer setBroadcast in screen-share mode
(the embedding/keyword router dropped it for non-English utterances) and make
its description force a tool call. "방송 꺼줘"->stop now 5/5; no false triggers.
- Stop the broadcast on voice leave (no orphaned stream).
- Security: bind VNC/noVNC to loopback by default (VNC_BIND override) and the
bridge to the container loopback (BRIDGE_HOST=127.0.0.1), not published.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Makes the all-in-one image actually run the new real-time-search features and
closes review gaps:
- Gemini OAuth path: install Node 22 + @google/gemini-cli (pinned 0.46.0) in the
image; mount a DEDICATED host dir (~/.config/javis/gemini) holding only the
OAuth creds to /root/.gemini (not the whole ~/.gemini). Verified in-container:
`gemini -p ... -o json` returns a grounded answer with no API key.
- Broadcast audio: add PulseAudio + a headless null-sink (run-pulse.sh, new
supervisor program); export XDG_RUNTIME_DIR/PULSE_SERVER so Chrome playback
and the selfbot `ffmpeg -f pulse -i @DEFAULT_MONITOR@` share one daemon.
Verified: default sink virtual_speaker, monitor present, ffmpeg capture OK.
- Bind the brain bridge to 127.0.0.1 only (internal, unauthenticated API).
- VNC host port is overridable; this server pins VNC_PORT=5902 (.env) since the
host already runs Xvnc on 5901.
Verified in-container with CDI GPU passthrough: RTX 5050 visible, NVENC
encoders (h264/hevc/av1) available.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
GPU acceleration is now on by default and verified end-to-end on the
Blackwell RTX 5050 (sm_120):
- Ollama offloads 100% to GPU (log: library=CUDA compute=12.0,
BLACKWELL_NATIVE_FP4=1). compose passes GPU via CDI
(devices: nvidia.com/gpu=all) to both ollama and javis.
- Whisper STT on GPU: faster-whisper>=1.1.0 + nvidia-cublas/cudnn cu12,
LD_LIBRARY_PATH baked into the image. Verified float16 transcribe on
sm_120; bridge auto-falls back to CPU when no GPU is present.
- Model: default chat model -> qwen3:8b (best 8GB-VRAM tool-calling,
~5GB Q4). Embed stays nomic-embed-text.
- README documents the host one-time setup (nvidia-container-toolkit +
`nvidia-ctk cdi generate`) and GPU on/off.
Verified: image builds; GPU visible in both containers via compose;
ollama ps = 100% GPU; faster-whisper cuda OK + CPU fallback OK;
bridge /health 200.
`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.
