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).
107 lines
4.7 KiB
YAML
107 lines
4.7 KiB
YAML
# ============================================================================
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# Javis Bot — Docker Compose
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# ollama : the LLM backend for the jarvis brain
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# ollama-init : one-shot, auto-pulls the chat + embed models on startup
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# javis : all-in-one container (VNC desktop + Chrome + bridge + bot)
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#
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# Just bring it up — everything (incl. Ollama models) comes up automatically:
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# docker compose up -d --build
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#
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# The Discord token can be added LAST: without it the desktop, brain bridge,
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# Ollama and models all run; only the bot waits. Then put DISCORD_BOT_TOKEN in
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# .env and re-run `docker compose up -d`.
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#
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# Watch the desktop: VNC viewer -> localhost:5901 (or browser -> localhost:6080)
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# ============================================================================
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services:
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ollama:
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image: ollama/ollama:latest
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restart: unless-stopped
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# Model residency is controlled per-request, not globally. The brain pins
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# the chat model with keep_alive=30m (src/jarvis/llm.py) so voice turns
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# never pay a cold reload, while embeddings pass keep_alive=0
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# (src/jarvis/memory/embeddings.py) so nomic-embed unloads right after use.
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# A global OLLAMA_KEEP_ALIVE=-1 was removed because it also kept the embed
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# model resident forever, wasting VRAM next to the chat model.
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volumes:
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- ollama_models:/root/.ollama
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# GPU: needs nvidia-container-toolkit on the host (CDI). Verified on the
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# RTX 5050 (Blackwell sm_120) — Ollama offloads 100% to GPU.
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devices:
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- "nvidia.com/gpu=all"
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# Auto-pull the models the brain needs, then exit. Idempotent (re-runnable).
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ollama-init:
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image: ollama/ollama:latest
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depends_on:
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- ollama
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restart: "no"
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environment:
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OLLAMA_HOST: http://ollama:11434
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CHAT_MODEL: ${OLLAMA_CHAT_MODEL:-qwen2.5:3b}
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EMBED_MODEL: ${OLLAMA_EMBED_MODEL:-nomic-embed-text}
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entrypoint: ["/bin/sh", "-c"]
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command:
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- |
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echo "[ollama-init] waiting for ollama server...";
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until ollama list >/dev/null 2>&1; do sleep 2; done;
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echo "[ollama-init] pulling $$CHAT_MODEL";
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ollama pull "$$CHAT_MODEL";
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echo "[ollama-init] pulling $$EMBED_MODEL";
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ollama pull "$$EMBED_MODEL";
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echo "[ollama-init] models ready.";
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javis:
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build: .
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restart: unless-stopped
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env_file:
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- path: .env
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required: false
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environment:
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# Point the brain at the ollama service and the bot at the in-container bridge.
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OLLAMA_BASE_URL: http://ollama:11434
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OLLAMA_CHAT_MODEL: ${OLLAMA_CHAT_MODEL:-qwen2.5:3b}
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OLLAMA_EMBED_MODEL: ${OLLAMA_EMBED_MODEL:-nomic-embed-text}
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WHISPER_MODEL: ${WHISPER_MODEL:-small}
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WHISPER_DEVICE: ${WHISPER_DEVICE:-cuda}
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WHISPER_COMPUTE_TYPE: ${WHISPER_COMPUTE_TYPE:-float16}
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BRIDGE_URL: http://127.0.0.1:8765
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depends_on:
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- ollama
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# GPU: accelerates Whisper STT (and anything else CUDA) in this container.
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# Verified: faster-whisper float16 works on the RTX 5050 (sm_120).
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devices:
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- "nvidia.com/gpu=all"
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shm_size: "1gb" # Chrome needs a larger /dev/shm
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ports:
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# All published to loopback only by default — VNC/noVNC use a weak default
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# password and the bridge is an unauthenticated internal API, so none
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# should be reachable off-host. For remote access use an SSH tunnel, or
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# set a strong VNC_PASSWORD and override the bind (e.g. VNC_BIND=0.0.0.0).
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# Host VNC port is overridable; this server already runs Xvnc on 5901 so
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# .env pins VNC_PORT=5902.
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- "${VNC_BIND:-127.0.0.1}:${VNC_PORT:-5901}:5901" # VNC
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- "${VNC_BIND:-127.0.0.1}:${NOVNC_PORT:-6080}:6080" # noVNC (browser)
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# The brain bridge is NOT published: it binds the container's loopback
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# (BRIDGE_HOST=127.0.0.1) and is only consumed by the bot in this same
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# container, so it needs no host port and stays unreachable off-container.
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volumes:
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- javis_data:/data # jarvis db + memory
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- whisper_cache:/root/.cache/huggingface # cached Whisper models
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- piper_voices:/opt/piper-voices # TTS voices
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# Gemini account login for GEMINI_AUTH=oauth real-time search. Mounts a
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# DEDICATED dir holding only the Gemini OAuth creds (not the whole
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# ~/.gemini), so the container can refresh its token without exposing
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# unrelated host state. Seed it once with the host login:
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# mkdir -p ~/.config/javis/gemini
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# cp ~/.gemini/oauth_creds.json ~/.config/javis/gemini/
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# Override GEMINI_OAUTH_DIR to point elsewhere. Only used when
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# GEMINI_AUTH=oauth.
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- ${GEMINI_OAUTH_DIR:-${HOME}/.config/javis/gemini}:/root/.gemini
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volumes:
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ollama_models:
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javis_data:
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whisper_cache:
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piper_voices:
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