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/services/projects.py

@@ -122,7 +122,7 @@ async def generate_project_summary(user_id: int, project_id: int) -> None:
         from fabledassistant.services.llm import generate_completion
         from fabledassistant.config import Config
         messages = [{"role": "user", "content": prompt}]
-        summary = await generate_completion(messages, model=Config.OLLAMA_MODEL, max_tokens=400)
+        summary = await generate_completion(messages, model=Config.OLLAMA_BACKGROUND_MODEL, max_tokens=400)
         if not summary:
             return