GPU support, parallel intent+context, and increased context window

Docker Compose:
- Enable Ollama GPU passthrough (nvidia, count: all) in both dev and prod files
- Add OLLAMA_FLASH_ATTENTION=1 (faster attention on GPU in both files)
- Add OLLAMA_MAX_LOADED_MODELS=2 and OLLAMA_KEEP_ALIVE=30m to prod (was already in dev)
- Remove 8G memory limit from prod Ollama service (CPU-bound constraint, no longer valid)

llm.py:
- Increase num_ctx 16384 → 32768 in stream_chat and stream_chat_with_tools (GPU VRAM allows it)
- Increase num_predict cap 4096 → 8192 for tool-augmented responses

generation_task.py:
- Parallelize build_context, get_tools_for_user, and get_setting all from the start
- As soon as tools list is ready (fast DB call), launch classify_intent as an asyncio.Task
- Await build_context and classify_intent together via asyncio.gather
- Intent result is pre-computed before the generation loop; loop just reads pre_intent on round 0
- intent_ms timing now reflects wall-clock time from intent start to completion

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

docker-compose.yml

@@ -35,15 +35,14 @@ services:
     environment:
       OLLAMA_MAX_LOADED_MODELS: "2"
       OLLAMA_KEEP_ALIVE: "30m"
-    # To enable GPU support, uncomment the deploy section below
-    # (requires nvidia-container-toolkit)
-    # deploy:
-    #   resources:
-    #     reservations:
-    #       devices:
-    #         - driver: nvidia
-    #           count: all
-    #           capabilities: [gpu]
+      OLLAMA_FLASH_ATTENTION: "1"
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: all
+              capabilities: [gpu]
 
 volumes:
   pgdata: