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/rerank.py

@@ -9,6 +9,8 @@ from typing import TYPE_CHECKING
 from python.ebook_search.llm_interface import request_rerank
 
 if TYPE_CHECKING:
+    import httpx
+
     from python.ebook_search.config import RerankConfig
     from python.ebook_search.search import SearchResult
 
@@ -23,7 +25,12 @@ class RerankResult:
     score: float
 
 
-def rerank_chunks(query: str, candidates: list[SearchResult], config: RerankConfig) -> list[SearchResult]:
+async def rerank_chunks(
+    client: httpx.AsyncClient,
+    query: str,
+    candidates: list[SearchResult],
+    config: RerankConfig,
+) -> list[SearchResult]:
     """Rerank candidates with a vLLM rerank endpoint."""
     if not candidates:
         return []
@@ -34,7 +41,7 @@ def rerank_chunks(query: str, candidates: list[SearchResult], config: RerankConf
         config.model,
         len(candidates),
     )
-    scores = score_candidates(query, candidates, config)
+    scores = await score_candidates(client, query, candidates, config)
     results = sorted(
         (
             replace(
@@ -56,13 +63,14 @@ def rerank_chunks(query: str, candidates: list[SearchResult], config: RerankConf
     return results
 
 
-def score_candidates(
+async def score_candidates(
+    client: httpx.AsyncClient,
     query: str,
     candidates: list[SearchResult],
     config: RerankConfig,
 ) -> dict[int, RerankResult]:
     """Score candidate chunks with the configured rerank API."""
-    body = request_rerank(query, [candidate.text for candidate in candidates], config)
+    body = await request_rerank(client, query, [candidate.text for candidate in candidates], config)
     if body is None:
         return zero_rerank_scores(candidates)