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).

src/jarvis/llm.py

@@ -41,8 +41,14 @@ def call_llm_direct(base_url: str, chat_model: str, system_prompt: str, user_con
         "stream": False,
         "options": options,
         "think": thinking,
+        # Keep the chat model resident between calls. Without an explicit
+        # keep_alive Ollama evicts it after its default idle window and the
+        # next turn pays a cold reload. We pin the chat model only (embeddings
+        # pass keep_alive=0 so they unload after use) instead of a global
+        # OLLAMA_KEEP_ALIVE=-1, which would keep every model resident forever.
+        "keep_alive": "30m",
     }
-    
+
     try:
         with requests.post(f"{base_url.rstrip('/')}/api/chat", json=payload, timeout=timeout_sec) as resp:
             resp.raise_for_status()
@@ -98,6 +104,8 @@ def call_llm_streaming(
         "stream": True,
         "options": {"num_ctx": 4096},
         "think": thinking,
+        # Keep the chat model resident between calls (see call_llm_direct).
+        "keep_alive": "30m",
     }
 
     # Use ``with`` so the streaming response (and the underlying TCP
@@ -201,6 +209,8 @@ def chat_with_messages(
         "stream": False,
         "options": {"num_ctx": 8192},
         "think": thinking,
+        # Keep the chat model resident between turns (see call_llm_direct).
+        "keep_alive": "30m",
     }
     if extra_options and isinstance(extra_options, dict):
         # Merge shallowly into options

src/jarvis/memory/embeddings.py

@@ -6,7 +6,11 @@ def get_embedding(text: str, base_url: str, model: str, timeout_sec: float = 15.
     try:
         resp = requests.post(
             f"{base_url.rstrip('/')}/api/embeddings",
-            json={"model": model, "prompt": text},
+            # keep_alive=0 unloads the embedding model right after the call so
+            # it does not sit resident in VRAM alongside the chat model. The
+            # chat model is pinned separately (llm.py keep_alive=30m); only the
+            # actively-used chat model should stay loaded.
+            json={"model": model, "prompt": text, "keep_alive": 0},
             timeout=timeout_sec,
         )
         resp.raise_for_status()