perf: run auxiliary LLM calls on a small model, big model only for the answer

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>

docker-compose.yml

@@ -40,6 +40,9 @@ services:
     environment:
       OLLAMA_HOST: http://ollama:11434
       CHAT_MODEL: ${OLLAMA_CHAT_MODEL:-qwen2.5:3b}
+      # Small auxiliary model for intent/router/extraction calls (see javis
+      # service). Pulled here so the split is ready out of the box.
+      INTENT_MODEL: ${OLLAMA_INTENT_MODEL:-qwen2.5:3b}
       EMBED_MODEL: ${OLLAMA_EMBED_MODEL:-nomic-embed-text}
     entrypoint: ["/bin/sh", "-c"]
     command:
@@ -48,6 +51,10 @@ services:
         until ollama list >/dev/null 2>&1; do sleep 2; done;
         echo "[ollama-init] pulling $$CHAT_MODEL";
         ollama pull "$$CHAT_MODEL";
+        if [ -n "$$INTENT_MODEL" ] && [ "$$INTENT_MODEL" != "$$CHAT_MODEL" ]; then
+          echo "[ollama-init] pulling $$INTENT_MODEL (auxiliary intent/router model)";
+          ollama pull "$$INTENT_MODEL";
+        fi;
         echo "[ollama-init] pulling $$EMBED_MODEL";
         ollama pull "$$EMBED_MODEL";
         echo "[ollama-init] models ready.";
@@ -62,6 +69,14 @@ services:
       # 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}
+      # Auxiliary small-model calls (intent judge, tool router, arg extraction,
+      # query decomposition) run on this fast model so the big chat model only
+      # runs for the actual spoken answer. With the GPU on, this is the main
+      # per-turn latency win: a command no longer pays the big model's cost 2-3
+      # times for routing/extraction. Defaults to qwen2.5:3b (the project's
+      # reference small model, clean Korean on classification); set it equal to
+      # OLLAMA_CHAT_MODEL to fold everything back onto one resident model.
+      OLLAMA_INTENT_MODEL: ${OLLAMA_INTENT_MODEL:-qwen2.5:3b}
       OLLAMA_EMBED_MODEL: ${OLLAMA_EMBED_MODEL:-nomic-embed-text}
       WHISPER_MODEL: ${WHISPER_MODEL:-medium}
       WHISPER_DEVICE: ${WHISPER_DEVICE:-cuda}