perf(brain): env to disable pre-loop planner; cut voice latency

Warm per-turn timing showed STT 0.1s, TTS ~1-3s, but the reply engine (LLM) was
8-17s — even a simple "고마워" took 16.7s — because it makes multiple model calls
per turn. Add a PLANNER_ENABLED env override (config.py) and default it to 0 in
the userbot compose so the pre-loop planner's extra LLM round-trip is dropped on
this latency-sensitive voice deployment. Also pins STT_LANGUAGE=ko in compose.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

docker-compose.yml

@@ -69,6 +69,11 @@ services:
       WHISPER_COMPUTE_TYPE: ${WHISPER_COMPUTE_TYPE:-float16}
       # Optional single-language lock for replies (empty = user's own language).
       OUTPUT_LANGUAGE: ${OUTPUT_LANGUAGE:-}
+      # 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}
       BRIDGE_URL: http://127.0.0.1:8765
     depends_on:
       - ollama

src/jarvis/config.py

@@ -710,7 +710,15 @@ def load_settings() -> Settings:
     else:
         evaluator_enabled = bool(_eval_raw)
     planner_model = str(merged.get("planner_model", "") or "").strip()
-    planner_enabled = bool(merged.get("planner_enabled", True))
+    # Env override (PLANNER_ENABLED=0/1) so a latency-sensitive voice deployment
+    # can drop the pre-loop planner LLM call without editing the config file.
+    _planner_env = os.environ.get("PLANNER_ENABLED", "").strip().lower()
+    if _planner_env in ("0", "false", "no", "off"):
+        planner_enabled = False
+    elif _planner_env in ("1", "true", "yes", "on"):
+        planner_enabled = True
+    else:
+        planner_enabled = bool(merged.get("planner_enabled", True))
     try:
         planner_timeout_sec = float(merged.get("planner_timeout_sec", 6.0))
     except (TypeError, ValueError):