test(usage): close round-5 nits (empty doc_types, consistency tidy-ups)

Round-5 claude-review (merge-ready; all nits):

- 🟡 Added test_empty_doc_types_normalizes_to_null pinning doc_types=[] → None
  in record_search_usage metadata (matches the None case).
- 🟡 record_search_usage docstring now notes nc_semantic_search_answer always
  meters with doc_types=None (it exposes no doc_types parameter).
- 🟢 BM25HybridSearchAlgorithm.__init__ now sets query_embedding /
  query_token_count alongside _embedded_query, so all three cache fields are
  instance attributes from construction (was relying on the class-level
  SearchAlgorithm defaults).
- 🟢 Ollama embed_batch_with_usage caches _dimension inline (mirrors
  OpenAI/Mistral), so the dimension is set via any embed path.
- 🟢 record_indexing_usage documents the independent-record / partial-failure
  semantics under SUM aggregation.

Deck #67.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

nextcloud_mcp_server/search/bm25_hybrid.py

@@ -56,9 +56,13 @@ class BM25HybridSearchAlgorithm(SearchAlgorithm):
         self.score_threshold = score_threshold
         self.fusion = models.Fusion.RRF if fusion == "rrf" else models.Fusion.DBSF
         self.fusion_name = fusion
-        # The query string whose dense embedding is cached in
-        # ``self.query_embedding`` — lets repeated search() calls on this
-        # per-request instance (the doc_types loop) reuse one embedding.
+        # Per-request query-embedding cache. ``_embedded_query`` is the query
+        # string whose dense embedding is held in ``query_embedding`` — repeated
+        # search() calls on this instance (the doc_types loop) reuse it. These
+        # shadow the class-level defaults on SearchAlgorithm; set here so all
+        # three cache fields are instance attributes from construction.
+        self.query_embedding: list[float] | None = None
+        self.query_token_count: int | None = None
         self._embedded_query: str | None = None
 
     @property