feat(ebook): migrate to async DB/HTTP and parallelize phrase pipeline

Convert the ebook-search web app to async end to end and add concurrency
to the protected-phrase extraction and judging pipeline so large books no
longer block the event loop or the UI.

ORM / infra:
- Add get_async_postgres_engine and factor shared URL/connect_args building
  into build_postgres_url (reused by the sync and async engine builders)
- Add async FastAPI session helpers (get_async_db, AsyncDbSession) with
  expire_on_commit=False to avoid implicit IO under asyncio

App:
- Use AsyncEngine/AsyncSession throughout routes, search, ingest, embeddings,
  answer, rerank and LLM calls; convert handlers to async
- Share a single httpx.AsyncClient in app state for LLM requests; size the
  connection pool for concurrent phrase-judging workers
- Add judge_tasks: run per-book judging as tracked background tasks so a
  book already being judged isn't double-queued

Protected phrases:
- Add a process pool (pool.py) and worker-count config
  (extraction/judge book/phrase workers) to parallelize candidate generation
  and judging
- Split admin actions into all/missing variants for generation and judging

Config:
- Add protected_phrase_extraction_workers, phrase_judge_book_workers,
  phrase_judge_phrase_workers

python/ebook_search/config.py

@@ -88,6 +88,9 @@ class EbookSearchConfig(BaseSettings):
     bm25_refresh_delay_seconds: int = 60
     protected_phrase_max_candidates_per_book: int = 5000
     protected_phrase_llm_candidates_per_book: int = 500
+    protected_phrase_extraction_workers: int = 16
+    phrase_judge_book_workers: int = 20
+    phrase_judge_phrase_workers: int = 100
     protected_phrase_confidence_threshold: float = 0.80
     phrase_matching_enabled: bool = True
     phrase_hit_boost: float = 0.25