Add models and database persistence for protected phrase extraction

- Introduced dataclasses for phrase candidates, judgments, and matches in `models.py`.
- Implemented database operations for candidate and protected phrases in `store.py`, including loading, saving, and deleting phrases.
- Enhanced text normalization functions in `text_normalization.py` with detailed docstrings.
- Refactored search functionality to utilize new models and methods for detecting protected phrases.

python/ebook_search/search.py

@@ -20,8 +20,7 @@ from python.ebook_search.bm25_corpus import (
     score_bm25_corpus,
 )
 from python.ebook_search.embeddings import MODEL_DIMENSIONS, embed_query, get_embedding_table
-from python.ebook_search.protected_phrases.lib import (
-    HydratedPhraseMatch,
+from python.ebook_search.protected_phrases.matching import (
     detect_protected_phrases_for_query,
     phrase_hits_for_chunks,
 )
@@ -40,6 +39,7 @@ if TYPE_CHECKING:
     from sqlalchemy.engine import Engine
 
     from python.ebook_search.config import EbookSearchConfig
+    from python.ebook_search.protected_phrases.models import HydratedPhraseMatch
 
 logger = logging.getLogger(__name__)