feat(ebook): add protected phrase extraction library with config-driven tuning

Refactor protected phrase handling from a single module into a
python/ebook_search/protected_phrases package covering extraction,
storage, and runtime matching. Phrase filtering is now data-driven via
bundled TOML files: ignored_phrases, bad_starts, bad_ends, and
most_common_words.

Add phrase-tuning settings to EbookSearchConfig so candidate generation,
scoring, LLM judging, and matching are configurable rather than hardcoded:
token bounds, entity token limit, raw n-gram min count, frequency and
chapter-spread score thresholds, candidate/LLM/target caps, confidence
threshold, nesting defaults, and the phrase hit boost.

python/ebook_search/config.py

@@ -86,6 +86,21 @@ class EbookSearchConfig(BaseSettings):
     validate_citations_enabled: bool = True
     bm25_index_dir: str = ".ebook_search_bm25"
     bm25_refresh_delay_seconds: int = 60
+    protected_phrase_max_candidates_per_book: int = 5000
+    protected_phrase_llm_candidates_per_book: int = 500
+    protected_phrase_confidence_threshold: float = 0.80
+    phrase_hit_boost: float = 0.25
+    phrase_min_tokens: int = 2
+    phrase_max_tokens: int = 5
+    phrase_max_entity_tokens: int = 8
+    phrase_raw_ngram_min_count: int = 2
+    phrase_raw_count_score_threshold: int = 3
+    phrase_raw_count_high_score_threshold: int = 10
+    phrase_chapter_count_score_threshold: int = 2
+    phrase_chapter_count_high_score_threshold: int = 5
+    phrase_target_protected_per_book: int = 100
+    phrase_default_allow_nested: bool = False
+    phrase_default_suppress_children: bool = True
 
     @field_validator("library_paths", mode="before")
     @classmethod