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/config.py

@@ -24,6 +24,10 @@ class Config:
     )
     OLLAMA_URL: str = os.environ.get("OLLAMA_URL", "http://localhost:11434")
     OLLAMA_MODEL: str = os.environ.get("OLLAMA_MODEL", "qwen3:latest")
+    # Lightweight model for background tasks (title generation, tag suggestions,
+    # project summaries, RSS classification). Using a separate model keeps the
+    # main model's KV cache intact between user messages, enabling prefix cache hits.
+    OLLAMA_BACKGROUND_MODEL: str = os.environ.get("OLLAMA_BACKGROUND_MODEL", "qwen2.5:0.5b")
     # KV cache context window for generation. Keep this as small as practical —
     # a larger context forces more KV cache into CPU RAM, drastically slowing prefill.
     # 16384 covers ~30+ message conversations with our system prompt comfortably.