Files
javis_bot/docker-compose.yml
javis-bot b91c05a355 perf(brain): pin chat model per-request, unload embeddings; default qwen2.5:3b
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).
2026-06-12 20:36:19 +09:00

107 lines
4.7 KiB
YAML

# ============================================================================
# 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 token can be added LAST: without it the desktop, brain bridge,
# Ollama and models all run; only the bot waits. Then put DISCORD_BOT_TOKEN in
# .env and re-run `docker compose up -d`.
#
# 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:-small}
WHISPER_DEVICE: ${WHISPER_DEVICE:-cuda}
WHISPER_COMPUTE_TYPE: ${WHISPER_COMPUTE_TYPE:-float16}
BRIDGE_URL: http://127.0.0.1:8765
depends_on:
- ollama
# GPU: accelerates Whisper STT (and anything else CUDA) in this container.
# Verified: faster-whisper float16 works on the RTX 5050 (sm_120).
devices:
- "nvidia.com/gpu=all"
shm_size: "1gb" # Chrome needs a larger /dev/shm
ports:
# All published to loopback only by default — VNC/noVNC use a weak default
# password and the bridge is an unauthenticated internal API, so none
# should be reachable off-host. For remote access use an SSH tunnel, or
# set a strong VNC_PASSWORD and override the bind (e.g. VNC_BIND=0.0.0.0).
# Host VNC port is overridable; this server already runs Xvnc on 5901 so
# .env pins VNC_PORT=5902.
- "${VNC_BIND:-127.0.0.1}:${VNC_PORT:-5901}:5901" # VNC
- "${VNC_BIND:-127.0.0.1}:${NOVNC_PORT:-6080}:6080" # noVNC (browser)
# 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: