feat(ai): semantic-search backfill worker + Ollama compose profile (RECOMMENDATIONS #4a)

First activation slice of semantic search (docs/discovery/05+06):
- EmbeddingBackfillWorker: fills Email.Embedding in small batches (32, 2s pause,
  newest first; subject+snippet, truncated) so interactive load is never starved.
  Exits immediately when the embedding provider is unavailable — AI-off deployments
  are untouched, Ollama hiccups back off instead of crashing the host.
- compose: optional 'ollama' service under the 'ai' profile (persistent model
  volume, commented GPU passthrough for the RTX 3080); api gets
  Ai__OllamaBaseUrl pointing at it.
- LiveDb test proves the worker persists 768-dim vectors against real pgvector
  (deterministic fake provider); LiveDb classes serialised into one xUnit
  collection (concurrent MigrateAsync on a fresh DB races — found while testing).

Verified against REAL Ollama locally: pulled nomic-embed-text in a container and
confirmed the /api/embeddings contract the provider uses returns 768-dim vectors.
Full suite 55 green (4 LiveDb vs real pgvector); format clean.

Next slice: hybrid RRF ranking in SearchService once embeddings exist.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

tests/InboxIntel.IntegrationTests/LiveDbSearchTests.cs

@@ -21,6 +21,7 @@ namespace InboxIntel.IntegrationTests;
 /// InMemory test run is unaffected.
 /// </summary>
 [Trait("Category", "LiveDb")]
+[Collection("LiveDb")] // serialise LiveDb classes: concurrent MigrateAsync on a fresh DB races
 public class LiveDbSearchTests
 {
     private static string? Conn => Environment.GetEnvironmentVariable("LIVEDB_CONNECTION");