fix(usage): embed query once across doc_types; address review round 1

Round-1 claude-review findings:

- 🔴 Multi-doc_type search billed N embedding calls as 1. nc_semantic_search
  loops search() once per doc_type on one BM25HybridSearchAlgorithm instance,
  and each call re-embedded the query, so only the last query_token_count was
  recorded. Cache the dense embedding per query on the (per-request) instance
  so the query is embedded — and metered — exactly once regardless of how many
  doc_types are searched. This also removes the redundant per-type embed work
  and avoids billing a user N× for one logical query.
- 🟡 Ollama embed() now delegates to embed_with_usage() so single and batch
  embeds use the same /api/embed endpoint (was the legacy /api/embeddings),
  keeping _detect_dimension and other embed() callers consistent.
- 🟢 round() instead of truncating int() when coercing provider-reported token
  counts (forward-compatible if a provider ever returns a float).

Tests: per-instance query-embedding cache (embedded once across 3 doc_types;
re-embeds on a different query).

Deferred (stated on the PR): mistral/openai single-embed dual path (changes
tested error/request semantics on the cloud-critical path — separate refactor),
bedrock boto3 sync-in-async (pre-existing; no new invoke_model calls per doc).

Deck #67.

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

nextcloud_mcp_server/server/semantic.py

@@ -537,9 +537,10 @@ def configure_semantic_tools(mcp: FastMCP):
             #
             # query_token_count is set by BM25HybridSearchAlgorithm during the
             # search() above. The doc_types loop reuses one search_algo instance
-            # for the same query string, so the final value is the single query
-            # embedding's cost (matches the prior one-query semantics). Falls
-            # back to 0 only if the embedding never ran (e.g. a pre-embed error).
+            # for the same query, and the algorithm caches the dense embedding
+            # per query, so the query is embedded — and metered — exactly once
+            # regardless of how many doc_types were searched. Falls back to 0
+            # only if the embedding never ran (e.g. a pre-embed error).
             #
             # Privacy note: user_id stays tenant-local. The CP rollup
             # aggregates GROUP BY (day, metric) into usage_daily, which has no