perf(llm): route background tasks to dedicated model to preserve KV cache

Background tasks (title generation, tag suggestions, project summaries,
RSS classification) were using qwen3:8b and wiping its KV cache after
every response, preventing prefix cache hits on subsequent user messages.

Adds OLLAMA_BACKGROUND_MODEL (default: qwen2.5:0.5b) config var and
routes all background LLM calls to it, keeping qwen3:8b's KV cache
warm between user messages for consistent sub-second TTFT.

Also adds infinite scroll to KnowledgeView (replaces load-more button)
and bakes spaCy en_core_web_sm into the Docker image to eliminate the
pip install on every startup.

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

src/fabledassistant/app.py

@@ -249,6 +249,7 @@ def create_app() -> Quart:
         # Also ensure the embedding model is pulled (no warm needed).
         asyncio.create_task(_warm_user_models())
         asyncio.create_task(_pull_model(Config.EMBEDDING_MODEL, warm=False))
+        asyncio.create_task(_pull_model(Config.OLLAMA_BACKGROUND_MODEL, warm=False))
 
         # After models are pulled, backfill embeddings for existing notes.
         # Runs in the background so it never blocks the server from accepting requests.