Follow-up to the OLLAMA_INTENT_MODEL split: document that the Docker stack runs
intent judging / tool routing / extraction on a small qwen2.5:3b (pulled by
ollama-init) kept separate from the big chat answer model, and that setting
OLLAMA_INTENT_MODEL = OLLAMA_CHAT_MODEL folds them 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>
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>
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>
Windows users following the docs hit "The system cannot find the file
specified" because COMPOSE_FILE's separator is OS-specific (':' collides
with the C: drive letter). Fix every Windows example to use ';', add an
explicit OS-separator warning in .env.example, README, DEPLOY.md and the
gpu-windows compose comment, and point users at the explicit `-f` form as
a separator-agnostic alternative.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Document that the base compose has no GPU and the GPU is enabled via an
OS-specific override (docker-compose.gpu-linux.yml CDI vs
docker-compose.gpu-windows.yml deploy-reservations), with per-OS host prep,
COMPOSE_FILE shortcut, CPU-only fallback, and Windows manual-run differences
(venv activation, ffmpeg, no .sh scripts / WSL2). Fix stale lines (GPU moved
out of base compose; default model qwen2.5:3b) and add MELO_DEVICE /
output_language to the env list.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
The "디스코드 토큰은 마지막에" section still told users to fill DISCORD_BOT_TOKEN
and call the `/자비스` slash command, but the default mode is the userbot
(selfbot) — a normal bot account cannot Go Live. Rewrite the setup to lead with
DISCORD_SELFBOT_TOKEN + DISCORD_VOICE_CHANNEL_ID and the text-command control
(`!자비스 join`/`leave`), with the burner-account ToS warning, and keep the
normal-bot path documented as the optional legacy alternative.
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.
