fix(embedding): instantiate BM25 singleton off the event loop

The ``BM25SparseEmbeddingProvider.__init__`` calls
``fastembed.SparseTextEmbedding(model_name="Qdrant/bm25")`` which
downloads ~50 MB of model weights from HuggingFace and loads them
into memory — observed >5 s wall-clock in production. The inference
methods (``encode_async``, ``encode_batch_async``) already wrap work
in ``anyio.to_thread.run_sync``, so the design intent is clearly to
keep FastEmbed off the event loop. That protection just didn't
cover the constructor.

Symptom in the Astrolabe Cloud per-tenant deploy (deck #102 smoke):
~30–90 s after a user enables semantic search, the pod tips into a
SIGKILL-restart cycle. Loki shows a single log line

  Initializing BM25 sparse embedding provider: Qdrant/bm25

followed by nothing else from the event loop until exitCode 137.
Kubernetes ``/health/live`` httpGet probe timeout=5s fires 6 times
in a row, kubelet kills the container, restart, repeat.

Fix: switch ``get_bm25_service()`` to an async accessor that wraps
the first-time construction in ``anyio.to_thread.run_sync``. Two
existing call sites (``vector/processor.py:603``,
``search/bm25_hybrid.py:123``) update to ``await``. Both are
already inside async functions so the await is free.

New unit test pins the invariant by monkey-patching
``BM25SparseEmbeddingProvider.__init__`` with ``time.sleep(1)`` and
asserting a concurrent ``anyio.sleep(0.05)`` finishes promptly —
the test fails if the constructor ever runs back on the event loop.

Same pattern exists in ``OllamaEmbeddingProvider.__init__`` (sync
``httpx.get`` health-check). Ollama isn't enabled in any current
deploy; filed as a follow-up.

Refs:
- Astrolabe Cloud deck card #102 (smoke discovery)
- Sibling fix #799 (NullPool for cross-loop-asyncpg, same class
  of "anyio bites you in production" bug)

Verified:
- ``uv run pytest tests/unit/`` — 1027 passed
- ``uv run ruff check`` clean on touched files
- ``uv run ty check`` clean on touched files
- New tests pass

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

nextcloud_mcp_server/vector/processor.py

@@ -600,7 +600,7 @@ async def _index_document(
                 "vector_sync.chunk_count": len(chunk_texts),
             },
         ):
-            bm25_service = get_bm25_service()
+            bm25_service = await get_bm25_service()
             sparse_embeddings = await bm25_service.encode_batch(chunk_texts)
 
     async def generate_highlights():