# ============================================================================ # Javis Bot — Docker Compose # ollama : the LLM backend for the jarvis brain # ollama-init : one-shot, auto-pulls the chat + embed models on startup # javis : all-in-one container (VNC desktop + Chrome + bridge + bot) # # Just bring it up — everything (incl. Ollama models) comes up automatically: # docker compose up -d --build # # The Discord credential can be added LAST: without it the desktop, brain # bridge, Ollama and models all run; only the bot waits. This deployment runs # in userbot mode, so put DISCORD_SELFBOT_TOKEN in .env and re-run # `docker compose up -d`. (A normal-bot DISCORD_BOT_TOKEN is optional and only # needed for the legacy slash-command bot; leave it blank for userbot mode.) # # Watch the desktop: VNC viewer -> localhost:5901 (or browser -> localhost:6080) # ============================================================================ services: ollama: image: ollama/ollama:latest restart: unless-stopped # Model residency is controlled per-request, not globally. The brain pins # the chat model with keep_alive=30m (src/jarvis/llm.py) so voice turns # never pay a cold reload, while embeddings pass keep_alive=0 # (src/jarvis/memory/embeddings.py) so nomic-embed unloads right after use. # A global OLLAMA_KEEP_ALIVE=-1 was removed because it also kept the embed # model resident forever, wasting VRAM next to the chat model. volumes: - ollama_models:/root/.ollama # GPU: needs nvidia-container-toolkit on the host (CDI). Verified on the # RTX 5050 (Blackwell sm_120) — Ollama offloads 100% to GPU. devices: - "nvidia.com/gpu=all" # Auto-pull the models the brain needs, then exit. Idempotent (re-runnable). ollama-init: image: ollama/ollama:latest depends_on: - ollama restart: "no" environment: OLLAMA_HOST: http://ollama:11434 CHAT_MODEL: ${OLLAMA_CHAT_MODEL:-qwen2.5:3b} EMBED_MODEL: ${OLLAMA_EMBED_MODEL:-nomic-embed-text} entrypoint: ["/bin/sh", "-c"] command: - | echo "[ollama-init] waiting for ollama server..."; until ollama list >/dev/null 2>&1; do sleep 2; done; echo "[ollama-init] pulling $$CHAT_MODEL"; ollama pull "$$CHAT_MODEL"; echo "[ollama-init] pulling $$EMBED_MODEL"; ollama pull "$$EMBED_MODEL"; echo "[ollama-init] models ready."; javis: build: . restart: unless-stopped env_file: - path: .env required: false environment: # Point the brain at the ollama service and the bot at the in-container bridge. OLLAMA_BASE_URL: http://ollama:11434 OLLAMA_CHAT_MODEL: ${OLLAMA_CHAT_MODEL:-qwen2.5:3b} OLLAMA_EMBED_MODEL: ${OLLAMA_EMBED_MODEL:-nomic-embed-text} WHISPER_MODEL: ${WHISPER_MODEL:-medium} WHISPER_DEVICE: ${WHISPER_DEVICE:-cuda} WHISPER_COMPUTE_TYPE: ${WHISPER_COMPUTE_TYPE:-float16} # MeloTTS on the GPU (cu128 torch baked by docker/setup-melo.sh). CPU synth # serialised under load and pushed TTS to 7-8s; GPU does ~0.3s/sentence. MELO_DEVICE: ${MELO_DEVICE:-cuda} # Optional single-language lock for replies (empty = user's own language). OUTPUT_LANGUAGE: ${OUTPUT_LANGUAGE:-ko} # Drop the pre-loop planner LLM call to cut voice-reply latency on small # hardware (the planner adds a full model round-trip per turn). PLANNER_ENABLED: ${PLANNER_ENABLED:-0} # Lock STT to Korean (skip Whisper auto-detect). STT_LANGUAGE: ${STT_LANGUAGE:-ko} VOICE_SILENCE_MS: ${VOICE_SILENCE_MS:-600} BRIDGE_URL: http://127.0.0.1:8765 # Split-deployment role: full (default, all-in-one), browser (only the # desktop + Chrome + CDP, reused over the LAN), or bot (only bot + bridge # + TTS, driving a remote browser via CDP_HOST). See docker/run-if-role.sh. JARVIS_ROLE: ${JARVIS_ROLE:-full} # Chrome CDP bind address INSIDE the container. 0.0.0.0 lets a remote bot # (JARVIS_ROLE=bot on another PC) drive this host's browser. Loopback by # default so the all-in-one layout stays unreachable off-host. CDP_BIND: ${CDP_BIND:-127.0.0.1} CDP_PORT: ${CDP_PORT:-9222} # Where the bot drives Chrome. Loopback for full/browser; on a remote bot # set CDP_HOST to the browser host's LAN IP (e.g. 192.168.10.9). CDP_HOST: ${CDP_HOST:-127.0.0.1} depends_on: - ollama # GPU: accelerates Whisper STT (and anything else CUDA) in this container. # Verified: faster-whisper float16 works on the RTX 5050 (sm_120). devices: - "nvidia.com/gpu=all" shm_size: "1gb" # Chrome needs a larger /dev/shm ports: # All published to loopback only by default — VNC/noVNC use a weak default # password and the bridge is an unauthenticated internal API, so none # should be reachable off-host. For remote access use an SSH tunnel, or # set a strong VNC_PASSWORD and override the bind (e.g. VNC_BIND=0.0.0.0). # Host VNC port is overridable; this server already runs Xvnc on 5901 so # .env pins VNC_PORT=5902. - "${VNC_BIND:-127.0.0.1}:${VNC_PORT:-5901}:5901" # VNC - "${VNC_BIND:-127.0.0.1}:${NOVNC_PORT:-6080}:6080" # noVNC (browser) # Chrome CDP for a remote bot (JARVIS_ROLE=bot). Loopback by default; for a # LAN browser-host set CDP_PUBLISH_BIND=0.0.0.0 (internal network, no auth). - "${CDP_PUBLISH_BIND:-127.0.0.1}:${CDP_PORT:-9222}:9222" # Chrome CDP # The brain bridge is NOT published: it binds the container's loopback # (BRIDGE_HOST=127.0.0.1) and is only consumed by the bot in this same # container, so it needs no host port and stays unreachable off-container. volumes: - javis_data:/data # jarvis db + memory - whisper_cache:/root/.cache/huggingface # cached Whisper models - piper_voices:/opt/piper-voices # TTS voices # Gemini account login for GEMINI_AUTH=oauth real-time search. Mounts a # DEDICATED dir holding only the Gemini OAuth creds (not the whole # ~/.gemini), so the container can refresh its token without exposing # unrelated host state. Seed it once with the host login: # mkdir -p ~/.config/javis/gemini # cp ~/.gemini/oauth_creds.json ~/.config/javis/gemini/ # Override GEMINI_OAUTH_DIR to point elsewhere. Only used when # GEMINI_AUTH=oauth. - ${GEMINI_OAUTH_DIR:-${HOME}/.config/javis/gemini}:/root/.gemini volumes: ollama_models: javis_data: whisper_cache: piper_voices: