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>

Dockerfile

@@ -19,7 +19,8 @@ RUN --mount=type=cache,target=/root/.cache/pip \
 # Install CPU-only torch first so pip doesn't pull full CUDA wheels (~2 GB) for kokoro/transformers.
 RUN --mount=type=cache,target=/root/.cache/pip \
     pip install torch --index-url https://download.pytorch.org/whl/cpu \
-    && pip install faster-whisper kokoro soundfile
+    && pip install faster-whisper kokoro soundfile \
+    && python -m spacy download en_core_web_sm
 
 # Build the fable-mcp wheel so it can be served for download
 COPY fable-mcp/ fable-mcp/