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/api/routes/admin.py

@@ -14,11 +14,8 @@ from python.ebook_search.api.dependencies import (
 from python.ebook_search.api.web import templates
 from python.ebook_search.embeddings import embed_missing_chunks, embedding_model_stats
 from python.ebook_search.ingest import ingest_configured_paths
-from python.ebook_search.protected_phrases.lib import (
+from python.ebook_search.protected_phrases.generate_ngrams import generate_candidate_phrases_for_books
-    build_missing_protected_phrases,
+from python.ebook_search.protected_phrases.judge_ngrams import judge_candidate_phrases_for_books
-    generate_candidate_phrases_for_books,
-    judge_candidate_phrases_for_books,
-)
 from python.fastapi_tools import DbSession  # noqa: TC001 FastAPI resolves this annotated dependency at runtime
 
 logger = logging.getLogger(__name__)
@@ -50,36 +47,6 @@ def scan_library(request: Request, config: AppConfig, session: DbSession) -> HTM
     return templates.TemplateResponse(request, "partials/admin_status.html", {"message": f"Indexed {count} EPUBs"})
 
 
-@router.post("/build-phrases", response_class=HTMLResponse)
-def build_phrases(request: Request, config: AppConfig, session: DbSession) -> HTMLResponse:
-    """Build protected phrases for indexed books that are missing them."""
-    try:
-        result = build_missing_protected_phrases(session, config)
-        session.commit()
-    except Exception as error:
-        session.rollback()
-        logger.exception("ebook_admin_build_phrases_failed")
-        return templates.TemplateResponse(request, "partials/error.html", {"message": str(error)}, status_code=500)
-
-    logger.info(
-        "ebook_admin_build_phrases_complete books_seen=%s books_built=%s protected=%s mentions=%s",
-        result.books_seen,
-        result.books_built,
-        result.protected_phrases,
-        result.phrase_mentions,
-    )
-    return templates.TemplateResponse(
-        request,
-        "partials/admin_status.html",
-        {
-            "message": (
-                f"Built phrases for {result.books_built} of {result.books_seen} books; "
-                f"{result.protected_phrases} protected phrases, {result.phrase_mentions} mentions"
-            )
-        },
-    )
-
-
 @router.post("/generate-ngrams", response_class=HTMLResponse)
 def generate_ngrams(request: Request, config: AppConfig, session: DbSession) -> HTMLResponse:
     """Generate candidate n-grams for indexed books without LLM judging."""

python/ebook_search/api/routes/page.py

@@ -12,7 +12,7 @@ from python.ebook_search.api.dependencies import (
     AppConfig,  # noqa: TC001 FastAPI resolves this annotated dependency at runtime
 )
 from python.ebook_search.api.web import templates
-from python.ebook_search.protected_phrases.lib import recalculate_candidate_phrases_for_book
+from python.ebook_search.protected_phrases.generate_ngrams import recalculate_candidate_phrases_for_book
 from python.fastapi_tools import DbSession  # noqa: TC001 FastAPI resolves this annotated dependency at runtime
 from python.orm.richie import EbookCandidatePhrase, EbookProtectedPhrase, EbookSource
 

python/ebook_search/ingest.py

@@ -13,7 +13,7 @@ import tiktoken
 from sqlalchemy import or_, select
 
 from python.ebook_search.epub_parse import parse_epub
-from python.ebook_search.protected_phrases.lib import index_chunk_phrase_mentions_for_book
+from python.ebook_search.protected_phrases.matching import index_chunk_phrase_mentions_for_book
 from python.orm.richie import EbookChapter, EbookChunk, EbookSource
 
 logger = logging.getLogger(__name__)

python/ebook_search/protected_phrases/extraction.py

@@ -0,0 +1,731 @@
+"""Candidate phrase extraction and scoring for protected phrases."""
+
+from __future__ import annotations
+
+import logging
+import re
+from collections import defaultdict
+from time import perf_counter
+from typing import TYPE_CHECKING, Protocol
+
+from yake import KeywordExtractor
+
+from python.ebook_search.protected_phrases.config import (
+    get_bad_ends,
+    get_bad_starts,
+    get_ignored_phrases,
+    get_most_common_words,
+)
+from python.ebook_search.protected_phrases.models import PhraseCandidate
+from python.ebook_search.protected_phrases.text_normalization import tokenize, tokenize_with_offsets
+
+if TYPE_CHECKING:
+    from collections.abc import Iterable, Mapping, Sequence
+
+    from python.ebook_search.config import EbookSearchConfig
+
+logger = logging.getLogger(__name__)
+
+
+BAD_START_SCORE_PENALTY = 3.0
+BAD_END_SCORE_PENALTY = 3.0
+SOURCE_FIELDS = (
+    "source_raw_ngram",
+    "source_yake",
+    "source_spacy_ner",
+    "source_spacy_noun_chunk",
+    "source_capitalized",
+    "source_metadata",
+)
+CAPITALIZED_PHRASE_RE = re.compile(r"\b(?:[A-Z][a-zA-Z']+)(?:\s+(?:of|the|and|in|on|for|[A-Z][a-zA-Z']+)){0,6}")
+
+
+class SpacySpan(Protocol):
+    """Small protocol for the spaCy span attributes used by this module."""
+
+    text: str
+
+
+class SpacyEntity(SpacySpan, Protocol):
+    """Small protocol for the spaCy entity attributes used by this module."""
+
+    label_: str
+
+
+class SpacyDoc(Protocol):
+    """Small protocol for the spaCy doc attributes used by this module."""
+
+    ents: Iterable[SpacyEntity]
+    noun_chunks: Iterable[SpacySpan]
+
+
+class SpacyLanguage(Protocol):
+    """Small protocol for a callable spaCy language pipeline."""
+
+    def __call__(self, text: str) -> SpacyDoc:
+        """Parse text into a spaCy-like doc."""
+
+
+class YakeExtractor(Protocol):
+    """Small protocol for the YAKE extractor used by this module."""
+
+    def extract_keywords(self, text: str) -> Iterable[tuple[str, float]]:
+        """Return YAKE keyword tuples."""
+
+
+class YakeExtractorFactory(Protocol):
+    """Callable constructor protocol for YAKE keyword extractors."""
+
+    def __call__(self, *, lan: str, n: int, dedupLim: float, top: int) -> YakeExtractor:  # noqa: N803
+        """Create a YAKE keyword extractor.
+
+        Args:
+            lan (str): Language code passed to YAKE.
+            n (int): Maximum n-gram size to extract.
+            dedupLim (float): Deduplication similarity threshold.
+            top (int): Maximum number of keyphrases to return.
+
+        Returns:
+            YakeExtractor: The constructed keyword extractor.
+        """
+
+
+def strip_leading_articles(phrase_norm: str) -> str:
+    """Remove one leading English article from a normalized phrase.
+
+    Args:
+        phrase_norm (str): Normalized phrase text to strip.
+
+    Returns:
+        str: The phrase with a single leading ``the``, ``a``, or ``an`` removed.
+    """
+    tokens_ = phrase_norm.split()
+    if tokens_ and tokens_[0] in {"the", "a", "an"}:
+        tokens_ = tokens_[1:]
+    return " ".join(tokens_)
+
+
+def normalize_candidate_phrase(
+    phrase_text: str,
+    config: EbookSearchConfig,
+    *,
+    min_tokens: int | None = None,
+    max_tokens: int | None = None,
+    strip_leading_article: bool = False,
+) -> tuple[str, str, int] | None:
+    """Normalize a candidate phrase and validate token bounds.
+
+    Args:
+        phrase_text (str): Raw phrase text to normalize.
+        config (EbookSearchConfig): Runtime phrase-tuning settings.
+        min_tokens (int | None): Minimum token count override; defaults to ``config.phrase_min_tokens``.
+        max_tokens (int | None): Maximum token count override; defaults to ``config.phrase_max_tokens``.
+        strip_leading_article (bool): Whether to drop a single leading English article.
+
+    Returns:
+        tuple[str, str, int] | None: Display text, normalized phrase, and token count, or ``None``
+        when the phrase falls outside the token bounds or is ignored.
+    """
+    normalized_tokens = tokenize_with_offsets(phrase_text)
+    start = 0
+    if strip_leading_article and normalized_tokens and normalized_tokens[0].text in {"the", "a", "an"}:
+        start = 1
+
+    selected_tokens = normalized_tokens[start:]
+    min_count = config.phrase_min_tokens if min_tokens is None else min_tokens
+    max_count = config.phrase_max_tokens if max_tokens is None else max_tokens
+    if len(selected_tokens) < min_count or len(selected_tokens) > max_count:
+        return None
+
+    phrase_norm = " ".join(token.text for token in selected_tokens)
+    if phrase_norm in get_ignored_phrases():
+        return None
+
+    display_text = phrase_text[selected_tokens[0].start_char : selected_tokens[-1].end_char].strip()
+    return display_text or phrase_norm, phrase_norm, len(selected_tokens)
+
+
+def extract_raw_ngrams(text: str, config: EbookSearchConfig) -> dict[str, PhraseCandidate]:
+    """Extract raw normalized n-grams as high-recall candidates.
+
+    Args:
+        text (str): Book text to slide n-gram windows over.
+        config (EbookSearchConfig): Runtime phrase-tuning settings.
+
+    Returns:
+        dict[str, PhraseCandidate]: Candidates keyed by normalized phrase, with raw counts.
+    """
+    tokens_ = tokenize(text)
+    out: dict[str, PhraseCandidate] = {}
+    for ngram_size in range(config.phrase_min_tokens, config.phrase_max_tokens + 1):
+        for start in range(len(tokens_) - ngram_size + 1):
+            normalized = normalize_candidate_phrase(" ".join(tokens_[start : start + ngram_size]), config)
+            if normalized is None:
+                continue
+            phrase_text, phrase_norm, token_count = normalized
+            item = out.setdefault(
+                phrase_norm,
+                PhraseCandidate(
+                    phrase_text=phrase_text,
+                    phrase_norm=phrase_norm,
+                    token_count=token_count,
+                    source_raw_ngram=True,
+                ),
+            )
+            item.raw_count += 1
+    return out
+
+
+
+
+def extract_yake_candidates(
+    book_text: str,
+    config: EbookSearchConfig,
+    top_k: int = 1000,
+) -> dict[str, PhraseCandidate]:
+    """Extract YAKE keyphrases when the optional YAKE package is installed.
+
+    Args:
+        book_text (str): Full book text to extract keyphrases from.
+        config (EbookSearchConfig): Runtime phrase-tuning settings.
+        top_k (int): Maximum number of YAKE keyphrases to request.
+
+    Returns:
+        dict[str, PhraseCandidate]: Candidates keyed by normalized phrase, with YAKE scores.
+    """
+    extractor = KeywordExtractor(lan="en", n=config.phrase_max_tokens, dedupLim=0.85, top=top_k)
+    out: dict[str, PhraseCandidate] = {}
+    for phrase_text, yake_score in extractor.extract_keywords(book_text):
+        normalized = normalize_candidate_phrase(phrase_text, config)
+        if normalized is None:
+            continue
+        display_text, phrase_norm, token_count = normalized
+        out[phrase_norm] = PhraseCandidate(
+            phrase_text=display_text,
+            phrase_norm=phrase_norm,
+            token_count=token_count,
+            source_yake=True,
+            yake_score=float(yake_score),
+        )
+    return out
+
+
+def extract_spacy_candidates(
+    book_text: str,
+    nlp: SpacyLanguage,
+    config: EbookSearchConfig,
+) -> dict[str, PhraseCandidate]:
+    """Extract spaCy named entities and noun chunks from one text block.
+
+    Args:
+        book_text (str): Text block to parse with spaCy.
+        nlp (SpacyLanguage): Callable spaCy language pipeline.
+        config (EbookSearchConfig): Runtime phrase-tuning settings.
+
+    Returns:
+        dict[str, PhraseCandidate]: Candidates keyed by normalized phrase from entities and noun chunks.
+    """
+    out: dict[str, PhraseCandidate] = {}
+    doc = nlp(book_text)
+
+    for ent in doc.ents:
+        normalized = normalize_candidate_phrase(
+            ent.text,
+            config,
+            max_tokens=config.phrase_max_entity_tokens,
+        )
+        if normalized is None:
+            continue
+        phrase_text, phrase_norm, token_count = normalized
+        out[phrase_norm] = PhraseCandidate(
+            phrase_text=phrase_text,
+            phrase_norm=phrase_norm,
+            token_count=token_count,
+            source_spacy_ner=True,
+            spacy_label=ent.label_,
+        )
+
+    for chunk in doc.noun_chunks:
+        normalized = normalize_candidate_phrase(chunk.text, config, strip_leading_article=True)
+        if normalized is None:
+            continue
+        phrase_text, phrase_norm, token_count = normalized
+        out[phrase_norm] = PhraseCandidate(
+            phrase_text=phrase_text,
+            phrase_norm=phrase_norm,
+            token_count=token_count,
+            source_spacy_noun_chunk=True,
+        )
+    return out
+
+
+def extract_capitalized_phrases(original_text: str, config: EbookSearchConfig) -> dict[str, PhraseCandidate]:
+    """Extract capitalized phrase runs that often carry fictional terms.
+
+    Args:
+        original_text (str): Original-case book text to scan for capitalized runs.
+        config (EbookSearchConfig): Runtime phrase-tuning settings.
+
+    Returns:
+        dict[str, PhraseCandidate]: Candidates keyed by normalized phrase from capitalized runs.
+    """
+    out: dict[str, PhraseCandidate] = {}
+    for match in CAPITALIZED_PHRASE_RE.finditer(original_text):
+        phrase_text = match.group(0).strip()
+        normalized = normalize_candidate_phrase(
+            phrase_text,
+            config,
+            max_tokens=config.phrase_max_entity_tokens,
+        )
+        if normalized is None:
+            continue
+        display_text, phrase_norm, token_count = normalized
+        out[phrase_norm] = PhraseCandidate(
+            phrase_text=display_text,
+            phrase_norm=phrase_norm,
+            token_count=token_count,
+            source_capitalized=True,
+        )
+    return out
+
+
+def extract_metadata_candidates(
+    metadata: Mapping[str, object] | None,
+    config: EbookSearchConfig,
+) -> dict[str, PhraseCandidate]:
+    """Extract phrases from book metadata values such as title, author, and series.
+
+    Args:
+        metadata (Mapping[str, object] | None): Book metadata values, or ``None`` when unavailable.
+        config (EbookSearchConfig): Runtime phrase-tuning settings.
+
+    Returns:
+        dict[str, PhraseCandidate]: Candidates keyed by normalized phrase from metadata values.
+    """
+    if metadata is None:
+        return {}
+
+    out: dict[str, PhraseCandidate] = {}
+    for value in metadata.values():
+        if value is None:
+            continue
+        phrase_text = str(value).strip()
+        normalized = normalize_candidate_phrase(
+            phrase_text,
+            config,
+            max_tokens=config.phrase_max_entity_tokens,
+        )
+        if normalized is None:
+            continue
+        display_text, phrase_norm, token_count = normalized
+        out[phrase_norm] = PhraseCandidate(
+            phrase_text=display_text,
+            phrase_norm=phrase_norm,
+            token_count=token_count,
+            source_metadata=True,
+        )
+    return out
+
+
+def merge_candidate_sources(*sources: Mapping[str, PhraseCandidate]) -> dict[str, PhraseCandidate]:
+    """Merge candidate dictionaries by normalized phrase.
+
+    Args:
+        *sources (Mapping[str, PhraseCandidate]): Candidate maps to combine, keyed by normalized phrase.
+
+    Returns:
+        dict[str, PhraseCandidate]: One merged candidate per normalized phrase.
+    """
+    merged: dict[str, PhraseCandidate] = {}
+    for source in sources:
+        for phrase_norm, item in source.items():
+            existing = merged.setdefault(
+                phrase_norm,
+                PhraseCandidate(
+                    phrase_text=item.phrase_text,
+                    phrase_norm=phrase_norm,
+                    token_count=item.token_count,
+                ),
+            )
+            merge_candidate(existing, item)
+    return merged
+
+
+def merge_candidate(existing: PhraseCandidate, item: PhraseCandidate) -> None:
+    """Merge one candidate into an existing candidate object.
+
+    Args:
+        existing (PhraseCandidate): Candidate mutated in place to absorb ``item``.
+        item (PhraseCandidate): Candidate whose sources, counts, and scores are merged in.
+    """
+    existing.source_raw_ngram = existing.source_raw_ngram or item.source_raw_ngram
+    existing.source_yake = existing.source_yake or item.source_yake
+    existing.source_spacy_ner = existing.source_spacy_ner or item.source_spacy_ner
+    existing.source_spacy_noun_chunk = existing.source_spacy_noun_chunk or item.source_spacy_noun_chunk
+    existing.source_capitalized = existing.source_capitalized or item.source_capitalized
+    existing.source_metadata = existing.source_metadata or item.source_metadata
+    existing.raw_count += item.raw_count
+    if item.yake_score is not None:
+        existing.yake_score = item.yake_score
+    if item.spacy_label:
+        existing.spacy_label = item.spacy_label
+
+
+def enrich_with_frequency_and_chapter_counts(
+    candidates: Mapping[str, PhraseCandidate],
+    chapters: Sequence[str],
+) -> dict[str, PhraseCandidate]:
+    """Add raw occurrence and chapter-spread counts to candidates.
+
+    Args:
+        candidates (Mapping[str, PhraseCandidate]): Candidates to enrich, keyed by normalized phrase.
+        chapters (Sequence[str]): Chapter-like text blocks used to count occurrences and spread.
+
+    Returns:
+        dict[str, PhraseCandidate]: Candidates with updated ``raw_count`` and ``chapter_count`` values.
+    """
+    if not candidates:
+        return {}
+
+    candidate_sets_by_size: dict[int, set[str]] = defaultdict(set)
+    for phrase_norm, candidate in candidates.items():
+        candidate_sets_by_size[candidate.token_count].add(phrase_norm)
+
+    total_counts: defaultdict[str, int] = defaultdict(int)
+    chapter_counts: defaultdict[str, int] = defaultdict(int)
+    for chapter in chapters:
+        seen_in_chapter: set[str] = set()
+        chapter_tokens = tokenize(chapter)
+        for ngram_size, candidate_norms in candidate_sets_by_size.items():
+            for start in range(len(chapter_tokens) - ngram_size + 1):
+                phrase_norm = " ".join(chapter_tokens[start : start + ngram_size])
+                if phrase_norm not in candidate_norms:
+                    continue
+                total_counts[phrase_norm] += 1
+                seen_in_chapter.add(phrase_norm)
+        for phrase_norm in seen_in_chapter:
+            chapter_counts[phrase_norm] += 1
+
+    enriched = dict(candidates)
+    for phrase_norm, candidate in enriched.items():
+        candidate.raw_count = max(candidate.raw_count, total_counts[phrase_norm])
+        candidate.chapter_count = chapter_counts[phrase_norm]
+    return enriched
+
+
+def filter_storable_candidates(
+    candidates: Mapping[str, PhraseCandidate],
+    config: EbookSearchConfig,
+) -> tuple[dict[str, PhraseCandidate], int, int, int]:
+    """Remove candidates that should not be persisted.
+
+    Args:
+        candidates (Mapping[str, PhraseCandidate]): Candidates to filter, keyed by normalized phrase.
+        config (EbookSearchConfig): Runtime phrase-tuning settings.
+
+    Returns:
+        tuple[dict[str, PhraseCandidate], int, int, int]: The storable candidates followed by the counts
+        dropped for being too short, too rare, and too common.
+    """
+    min_raw_count = minimum_candidate_raw_count(config)
+    filtered: dict[str, PhraseCandidate] = {}
+    too_short = 0
+    too_rare = 0
+    too_common = 0
+    for phrase_norm, candidate in candidates.items():
+        if candidate.token_count < config.phrase_min_tokens:
+            too_short += 1
+            continue
+        if candidate.raw_count < min_raw_count:
+            too_rare += 1
+            continue
+        if is_most_common_word_phrase(phrase_norm):
+            too_common += 1
+            continue
+        filtered[phrase_norm] = candidate
+    return filtered, too_short, too_rare, too_common
+
+
+def minimum_candidate_raw_count(config: EbookSearchConfig) -> int:
+    """Return the minimum occurrence count required before storing a candidate.
+
+    Args:
+        config (EbookSearchConfig): Runtime phrase-tuning settings.
+
+    Returns:
+        int: The minimum raw occurrence count, never less than 1.
+    """
+    return max(config.phrase_raw_ngram_min_count, 1)
+
+
+def is_most_common_word_phrase(phrase_norm: str) -> bool:
+    """Return whether every token in a normalized phrase is a common word.
+
+    Args:
+        phrase_norm (str): Normalized phrase text to inspect.
+
+    Returns:
+        bool: True when the phrase is non-empty and every token is a common word.
+    """
+    tokens_ = phrase_norm.split()
+    common_words = get_most_common_words()
+    return bool(tokens_) and all(token in common_words for token in tokens_)
+
+
+def score_candidate(candidate: PhraseCandidate, config: EbookSearchConfig) -> float:
+    """Score a phrase candidate before LLM judging.
+
+    Args:
+        candidate (PhraseCandidate): Candidate to score.
+        config (EbookSearchConfig): Runtime phrase-tuning settings.
+
+    Returns:
+        float: Combined score from sources, frequency, and length, less any penalties.
+    """
+    score = source_score(candidate) + frequency_score(candidate, config) + token_count_score(candidate, config)
+    if candidate.phrase_norm in get_ignored_phrases():
+        score -= 100.0
+    if has_bad_start(candidate.phrase_norm):
+        score -= BAD_START_SCORE_PENALTY
+    if has_bad_end(candidate.phrase_norm):
+        score -= BAD_END_SCORE_PENALTY
+    return score
+
+
+def has_bad_start(phrase_norm: str) -> bool:
+    """Return whether a normalized phrase starts with a bad starting token.
+
+    Args:
+        phrase_norm (str): Normalized phrase text to inspect.
+
+    Returns:
+        bool: True when the first token is a known bad starting token.
+    """
+    tokens_ = phrase_norm.split()
+    return bool(tokens_ and tokens_[0] in get_bad_starts())
+
+
+def has_bad_end(phrase_norm: str) -> bool:
+    """Return whether a normalized phrase ends with a bad ending token.
+
+    Args:
+        phrase_norm (str): Normalized phrase text to inspect.
+
+    Returns:
+        bool: True when the last token is a known bad ending token.
+    """
+    tokens_ = phrase_norm.split()
+    return bool(tokens_ and tokens_[-1] in get_bad_ends())
+
+
+def source_score(candidate: PhraseCandidate) -> float:
+    """Return the score contribution from extraction sources.
+
+    Args:
+        candidate (PhraseCandidate): Candidate whose enabled sources are weighted.
+
+    Returns:
+        float: Summed weight of the candidate's enabled extraction sources.
+    """
+    return sum(
+        weight
+        for enabled, weight in (
+            (candidate.source_yake, 2.0),
+            (candidate.source_spacy_ner, 2.5),
+            (candidate.source_spacy_noun_chunk, 1.5),
+            (candidate.source_capitalized, 2.0),
+            (candidate.source_raw_ngram, 0.5),
+        )
+        if enabled
+    )
+
+
+def frequency_score(candidate: PhraseCandidate, config: EbookSearchConfig) -> float:
+    """Return the score contribution from frequency and chapter spread.
+
+    Args:
+        candidate (PhraseCandidate): Candidate whose counts are scored.
+        config (EbookSearchConfig): Runtime phrase-tuning settings holding score thresholds.
+
+    Returns:
+        float: Summed weight for each frequency and chapter-spread threshold the candidate meets.
+    """
+    return sum(
+        weight
+        for count, threshold, weight in (
+            (candidate.raw_count, config.phrase_raw_count_score_threshold, 1.0),
+            (candidate.raw_count, config.phrase_raw_count_high_score_threshold, 1.0),
+            (candidate.chapter_count, config.phrase_chapter_count_score_threshold, 1.0),
+            (candidate.chapter_count, config.phrase_chapter_count_high_score_threshold, 1.0),
+        )
+        if count >= threshold
+    )
+
+
+def token_count_score(candidate: PhraseCandidate, config: EbookSearchConfig) -> float:
+    """Return the score contribution from phrase length.
+
+    Args:
+        candidate (PhraseCandidate): Candidate whose token count is scored.
+        config (EbookSearchConfig): Runtime phrase-tuning settings holding the max token bound.
+
+    Returns:
+        float: Length-based score contribution, which may be negative for over- or under-length phrases.
+    """
+    if candidate.token_count == 1:
+        return -0.5
+    if candidate.token_count in {2, 3, 4}:
+        return 0.5
+    if candidate.token_count > config.phrase_max_tokens:
+        return -1.0
+    return 0.0
+
+
+def get_sample_contexts(normalized_book_text: str, phrase_norm: str, max_contexts: int = 5) -> list[str]:
+    """Return normalized context snippets containing a candidate phrase.
+
+    ``normalized_book_text`` is expected to already be ``normalize_text``-ed by the caller
+    so the whole book is not re-normalized for every phrase.
+
+    Args:
+        normalized_book_text (str): Whole book text, already normalized, to search.
+        phrase_norm (str): Normalized phrase to find contexts around.
+        max_contexts (int): Maximum number of context snippets to return.
+
+    Returns:
+        list[str]: Up to ``max_contexts`` normalized snippets surrounding the phrase.
+    """
+    contexts: list[str] = []
+    start = 0
+    while len(contexts) < max_contexts:
+        index = normalized_book_text.find(phrase_norm, start)
+        if index == -1:
+            break
+        left = max(0, index - 300)
+        right = min(len(normalized_book_text), index + len(phrase_norm) + 300)
+        contexts.append(normalized_book_text[left:right])
+        start = index + len(phrase_norm)
+    return contexts
+
+
+def candidate_source_names(candidate: PhraseCandidate) -> list[str]:
+    """Return enabled source names for an extracted candidate.
+
+    Args:
+        candidate (PhraseCandidate): Candidate whose enabled sources are listed.
+
+    Returns:
+        list[str]: Names of the extraction sources that produced the candidate.
+    """
+    names: list[str] = []
+    if candidate.source_raw_ngram:
+        names.append("raw_ngram")
+    if candidate.source_yake:
+        names.append("yake")
+    if candidate.source_spacy_ner:
+        names.append("spacy_ner")
+    if candidate.source_spacy_noun_chunk:
+        names.append("spacy_noun_chunk")
+    if candidate.source_capitalized:
+        names.append("capitalized")
+    if candidate.source_metadata:
+        names.append("metadata")
+    return names
+
+
+def extract_phrase_candidates_for_book(
+    book_text: str,
+    chapters: Sequence[str],
+    config: EbookSearchConfig,
+    *,
+    nlp: SpacyLanguage | None = None,
+    metadata: Mapping[str, object] | None = None,
+) -> list[PhraseCandidate]:
+    """Extract, score, and limit phrase candidates for one book.
+
+    Args:
+        book_text (str): Full book text used for most extraction sources.
+        chapters (Sequence[str]): Chapter-like text blocks used for spaCy and frequency counts.
+        config (EbookSearchConfig): Runtime phrase-tuning settings.
+        nlp (SpacyLanguage | None): Optional spaCy pipeline for entity and noun-chunk sources.
+        metadata (Mapping[str, object] | None): Optional book metadata used as a candidate source.
+
+    Returns:
+        list[PhraseCandidate]: Scored candidates sorted best-first and capped per book.
+    """
+    started_at = perf_counter()
+    logger.info(
+        "ebook_phrase_candidate_extract_start chapters=%s chars=%s min_tokens=%s max_tokens=%s max_candidates=%s",
+        len(chapters),
+        len(book_text),
+        config.phrase_min_tokens,
+        config.phrase_max_tokens,
+        config.protected_phrase_max_candidates_per_book,
+    )
+    raw_started_at = perf_counter()
+    raw = extract_raw_ngrams(book_text, config)
+    logger.info(
+        "ebook_phrase_candidate_extract_raw_complete candidates=%s duration_ms=%.1f",
+        len(raw),
+        (perf_counter() - raw_started_at) * 1000,
+    )
+    yake_started_at = perf_counter()
+    yake_candidates = extract_yake_candidates(book_text, config)
+    logger.info(
+        "ebook_phrase_candidate_extract_yake_complete candidates=%s duration_ms=%.1f",
+        len(yake_candidates),
+        (perf_counter() - yake_started_at) * 1000,
+    )
+    spacy_candidates: dict[str, PhraseCandidate] = {}
+    if nlp is not None:
+        spacy_started_at = perf_counter()
+        for chapter in chapters:
+            spacy_candidates = merge_candidate_sources(spacy_candidates, extract_spacy_candidates(chapter, nlp, config))
+        logger.info(
+            "ebook_phrase_candidate_extract_spacy_complete candidates=%s duration_ms=%.1f",
+            len(spacy_candidates),
+            (perf_counter() - spacy_started_at) * 1000,
+        )
+    capitalized_started_at = perf_counter()
+    capitalized = extract_capitalized_phrases(book_text, config)
+    logger.info(
+        "ebook_phrase_candidate_extract_capitalized_complete candidates=%s duration_ms=%.1f",
+        len(capitalized),
+        (perf_counter() - capitalized_started_at) * 1000,
+    )
+    metadata_candidates = extract_metadata_candidates(metadata, config)
+
+    candidates = merge_candidate_sources(raw, yake_candidates, spacy_candidates, capitalized, metadata_candidates)
+    enriched_started_at = perf_counter()
+    candidates = enrich_with_frequency_and_chapter_counts(candidates, chapters)
+    pre_filter_count = len(candidates)
+    candidates, filtered_too_short, filtered_too_rare, filtered_too_common = filter_storable_candidates(
+        candidates,
+        config,
+    )
+    for candidate in candidates.values():
+        candidate.candidate_score = score_candidate(candidate, config)
+
+    limited = sorted(candidates.values(), key=lambda item: item.candidate_score, reverse=True)[
+        : config.protected_phrase_max_candidates_per_book
+    ]
+    logger.info(
+        "ebook_phrase_candidate_extract_complete raw=%s yake=%s spacy=%s capitalized=%s metadata=%s "
+        "merged=%s filtered_too_short=%s filtered_too_rare=%s filtered_too_common=%s min_uses=%s "
+        "storable=%s limited=%s enrich_score_ms=%.1f duration_ms=%.1f",
+        len(raw),
+        len(yake_candidates),
+        len(spacy_candidates),
+        len(capitalized),
+        len(metadata_candidates),
+        pre_filter_count,
+        filtered_too_short,
+        filtered_too_rare,
+        filtered_too_common,
+        minimum_candidate_raw_count(config),
+        len(candidates),
+        len(limited),
+        (perf_counter() - enriched_started_at) * 1000,
+        (perf_counter() - started_at) * 1000,
+    )
+    return limited

python/ebook_search/protected_phrases/generate_ngrams.py

@@ -0,0 +1,274 @@
+"""Book-level orchestration for candidate n-gram generation and recalculation."""
+
+from __future__ import annotations
+
+import logging
+from time import perf_counter
+from typing import TYPE_CHECKING
+
+from sqlalchemy import select
+
+from python.ebook_search.protected_phrases.extraction import extract_phrase_candidates_for_book
+from python.ebook_search.protected_phrases.models import (
+    BookCandidateResult,
+    PhraseCandidateGenerationResult,
+    PhraseRecalculationResult,
+)
+from python.ebook_search.protected_phrases.store import (
+    delete_phrase_data_for_book,
+    load_book_chapter_texts,
+    metadata_for_source,
+    prune_unstorable_unjudged_candidate_phrases,
+    save_candidate_to_db,
+)
+from python.orm.richie import EbookSource
+
+if TYPE_CHECKING:
+    from collections.abc import Mapping, Sequence
+
+    from sqlalchemy.orm import Session
+
+    from python.ebook_search.config import EbookSearchConfig
+    from python.ebook_search.protected_phrases.extraction import SpacyLanguage
+    from python.orm.richie import EbookCandidatePhrase
+
+logger = logging.getLogger(__name__)
+
+
+def generate_candidate_phrases_for_books(
+    session: Session,
+    config: EbookSearchConfig,
+    *,
+    nlp: SpacyLanguage | None = None,
+) -> PhraseCandidateGenerationResult:
+    """Create or refresh candidate phrases for indexed books without calling the LLM judge.
+
+    Args:
+        session (Session): Active database session.
+        config (EbookSearchConfig): Runtime phrase-tuning settings.
+        nlp (SpacyLanguage | None): Optional spaCy pipeline for entity and noun-chunk sources.
+
+    Returns:
+        PhraseCandidateGenerationResult: Per-corpus counts of books seen, built, and candidates stored.
+    """
+    sources = session.scalars(select(EbookSource).order_by(EbookSource.id)).all()
+    books_seen = len(sources)
+    logger.info(
+        "ebook_candidate_phrase_generation_start books_seen=%s min_tokens=%s max_tokens=%s max_candidates_per_book=%s",
+        books_seen,
+        config.phrase_min_tokens,
+        config.phrase_max_tokens,
+        config.protected_phrase_max_candidates_per_book,
+    )
+
+    outcomes = [generate_candidates_for_source(session, source, config, nlp=nlp) for source in sources]
+
+    result = PhraseCandidateGenerationResult(
+        books_seen=books_seen,
+        books_built=sum(1 for outcome in outcomes if outcome.built),
+        candidate_phrases=sum(outcome.candidates for outcome in outcomes),
+    )
+    logger.info(
+        "ebook_candidate_phrase_generation_complete books_seen=%s books_built=%s candidate_total=%s",
+        result.books_seen,
+        result.books_built,
+        result.candidate_phrases,
+    )
+    return result
+
+
+def generate_candidates_for_source(
+    session: Session,
+    source: EbookSource,
+    config: EbookSearchConfig,
+    *,
+    nlp: SpacyLanguage | None = None,
+) -> BookCandidateResult:
+    """Generate and store candidate phrases for one book, managing its own transaction.
+
+    Commits on success; rolls back and re-raises on error so callers stop the backfill.
+
+    Args:
+        session (Session): Active database session.
+        source (EbookSource): Indexed book to generate candidates for.
+        config (EbookSearchConfig): Runtime phrase-tuning settings.
+        nlp (SpacyLanguage | None): Optional spaCy pipeline for entity and noun-chunk sources.
+
+    Returns:
+        BookCandidateResult: Candidate count and whether the book was committed.
+    """
+    book_started_at = perf_counter()
+    logger.info(
+        "ebook_candidate_phrase_generation_book_start source_id=%s title=%r",
+        source.id,
+        source.title,
+    )
+    try:
+        chapters = load_book_chapter_texts(session, source.id)
+        if not chapters:
+            logger.warning("ebook_candidate_phrase_generation_book_empty source_id=%s", source.id)
+            return BookCandidateResult()
+        book_text = "\n\n".join(chapters)
+        logger.info(
+            "ebook_candidate_phrase_generation_book_loaded source_id=%s chapters=%s chars=%s",
+            source.id,
+            len(chapters),
+            len(book_text),
+        )
+
+        candidates = generate_candidate_phrases_for_book(
+            session,
+            source.id,
+            series_id=None,
+            book_text=book_text,
+            chapters=chapters,
+            config=config,
+            nlp=nlp,
+            metadata=metadata_for_source(source),
+        )
+        session.commit()
+    except Exception:
+        session.rollback()
+        logger.exception("ebook_candidate_phrase_generation_book_failed source_id=%s", source.id)
+        raise
+
+    logger.info(
+        "ebook_candidate_phrase_generation_book_committed source_id=%s candidates=%s duration_ms=%.1f",
+        source.id,
+        len(candidates),
+        (perf_counter() - book_started_at) * 1000,
+    )
+    return BookCandidateResult(candidates=len(candidates), built=True)
+
+
+def recalculate_candidate_phrases_for_book(
+    session: Session,
+    source: EbookSource,
+    config: EbookSearchConfig,
+    *,
+    nlp: SpacyLanguage | None = None,
+) -> PhraseRecalculationResult:
+    """Remove all book phrase data, regenerate candidates, and commit the completed book.
+
+    Args:
+        session (Session): Active database session.
+        source (EbookSource): Indexed book to recalculate.
+        config (EbookSearchConfig): Runtime phrase-tuning settings.
+        nlp (SpacyLanguage | None): Optional spaCy pipeline for entity and noun-chunk sources.
+
+    Returns:
+        PhraseRecalculationResult: Deleted-row counts and the number of candidates regenerated.
+    """
+    started_at = perf_counter()
+    logger.info(
+        "ebook_candidate_phrase_recalculation_start source_id=%s title=%r",
+        source.id,
+        source.title,
+    )
+    try:
+        deleted = delete_phrase_data_for_book(session, source.id)
+        chapters = load_book_chapter_texts(session, source.id)
+        if not chapters:
+            logger.warning("ebook_candidate_phrase_recalculation_book_empty source_id=%s", source.id)
+            session.commit()
+            return PhraseRecalculationResult(
+                book_id=source.id,
+                deleted_candidates=deleted.deleted_candidates,
+                deleted_protected_phrases=deleted.deleted_protected_phrases,
+                deleted_aliases=deleted.deleted_aliases,
+                deleted_mentions=deleted.deleted_mentions,
+                candidate_phrases=0,
+            )
+
+        candidates = generate_candidate_phrases_for_book(
+            session,
+            source.id,
+            series_id=None,
+            book_text="\n\n".join(chapters),
+            chapters=chapters,
+            config=config,
+            nlp=nlp,
+            metadata=metadata_for_source(source),
+        )
+        session.commit()
+    except Exception:
+        session.rollback()
+        logger.exception("ebook_candidate_phrase_recalculation_failed source_id=%s", source.id)
+        raise
+
+    result = PhraseRecalculationResult(
+        book_id=source.id,
+        deleted_candidates=deleted.deleted_candidates,
+        deleted_protected_phrases=deleted.deleted_protected_phrases,
+        deleted_aliases=deleted.deleted_aliases,
+        deleted_mentions=deleted.deleted_mentions,
+        candidate_phrases=len(candidates),
+    )
+    logger.info(
+        "ebook_candidate_phrase_recalculation_complete source_id=%s deleted_candidates=%s "
+        "deleted_protected=%s deleted_aliases=%s deleted_mentions=%s candidates=%s duration_ms=%.1f",
+        source.id,
+        result.deleted_candidates,
+        result.deleted_protected_phrases,
+        result.deleted_aliases,
+        result.deleted_mentions,
+        result.candidate_phrases,
+        (perf_counter() - started_at) * 1000,
+    )
+    return result
+
+
+def generate_candidate_phrases_for_book(
+    session: Session,
+    book_id: int,
+    series_id: int | None,
+    book_text: str,
+    chapters: Sequence[str],
+    config: EbookSearchConfig,
+    *,
+    nlp: SpacyLanguage | None = None,
+    metadata: Mapping[str, object] | None = None,
+) -> list[EbookCandidatePhrase]:
+    """Extract and store candidate phrases for one book without LLM judging.
+
+    Args:
+        session (Session): Active database session.
+        book_id (int): Book the candidates belong to.
+        series_id (int | None): Series scope for the stored candidates.
+        book_text (str): Full book text used for extraction.
+        chapters (Sequence[str]): Chapter-like text blocks used for frequency counts.
+        config (EbookSearchConfig): Runtime phrase-tuning settings.
+        nlp (SpacyLanguage | None): Optional spaCy pipeline for entity and noun-chunk sources.
+        metadata (Mapping[str, object] | None): Optional book metadata used as a candidate source.
+
+    Returns:
+        list[EbookCandidatePhrase]: The stored candidate phrase rows.
+    """
+    started_at = perf_counter()
+    limited_candidates = extract_phrase_candidates_for_book(
+        book_text,
+        chapters,
+        config,
+        nlp=nlp,
+        metadata=metadata,
+    )
+    save_started_at = perf_counter()
+    pruned_count = prune_unstorable_unjudged_candidate_phrases(session, book_id, config)
+    logger.info(
+        "ebook_candidate_phrase_save_start book_id=%s candidates=%s pruned_unstorable=%s",
+        book_id,
+        len(limited_candidates),
+        pruned_count,
+    )
+    rows = [
+        save_candidate_to_db(session, book_id, series_id, candidate, judgment=None) for candidate in limited_candidates
+    ]
+    session.flush()
+    logger.info(
+        "ebook_candidate_phrase_generation_complete book_id=%s candidates=%s save_ms=%.1f duration_ms=%.1f",
+        book_id,
+        len(rows),
+        (perf_counter() - save_started_at) * 1000,
+        (perf_counter() - started_at) * 1000,
+    )
+    return rows

python/ebook_search/protected_phrases/judge_ngrams.py

@@ -0,0 +1,481 @@
+"""Book-level orchestration for LLM judging and promotion of candidate phrases."""
+
+from __future__ import annotations
+
+import json
+import logging
+import re
+from time import perf_counter
+from typing import TYPE_CHECKING
+
+from sqlalchemy import select
+
+from python.ebook_search.llm_interface import request_chat_completion
+from python.ebook_search.protected_phrases.extraction import (
+    candidate_source_names,
+    get_sample_contexts,
+    is_most_common_word_phrase,
+    minimum_candidate_raw_count,
+)
+from python.ebook_search.protected_phrases.matching import index_chunk_phrase_mentions_for_book
+from python.ebook_search.protected_phrases.models import BookJudgmentResult, LLMJudgment, PhraseJudgmentBackfillResult
+from python.ebook_search.protected_phrases.store import (
+    count_protected_phrases,
+    count_unjudged_candidates,
+    load_book_text,
+    load_candidates_for_judgment,
+    phrase_candidate_from_row,
+    save_candidate_to_db,
+    upsert_protected_phrase,
+)
+from python.ebook_search.protected_phrases.text_normalization import normalize_text
+from python.orm.richie import EbookSource
+
+if TYPE_CHECKING:
+    from sqlalchemy.orm import Session
+
+    from python.ebook_search.config import EbookSearchConfig
+    from python.ebook_search.protected_phrases.models import PhraseCandidate
+    from python.orm.richie import EbookCandidatePhrase, EbookProtectedPhrase
+
+JSON_OBJECT_RE = re.compile(r"\{.*\}", re.DOTALL)
+
+
+
+logger = logging.getLogger(__name__)
+
+
+def judge_candidate_phrases_for_books(
+    session: Session,
+    config: EbookSearchConfig,
+) -> PhraseJudgmentBackfillResult:
+    """Judge stored candidate phrases and promote accepted phrases for indexed books.
+
+    Args:
+        session (Session): Active database session.
+        config (EbookSearchConfig): Runtime phrase-tuning settings.
+
+    Returns:
+        PhraseJudgmentBackfillResult: Per-corpus counts of books judged, failures, candidates,
+        protected phrases, and mentions.
+    """
+    source_ids = session.scalars(select(EbookSource.id).order_by(EbookSource.id)).all()
+    books_seen = len(source_ids)
+    logger.info(
+        "ebook_candidate_phrase_judgment_start books_seen=%s llm_candidates_per_book=%s "
+        "target_protected_per_book=%s confidence_threshold=%.2f min_tokens=%s min_uses=%s",
+        books_seen,
+        config.protected_phrase_llm_candidates_per_book,
+        config.phrase_target_protected_per_book,
+        config.protected_phrase_confidence_threshold,
+        config.phrase_min_tokens,
+        minimum_candidate_raw_count(config),
+    )
+
+    outcomes = [judge_book_for_backfill(session, source_id, config) for source_id in source_ids]
+
+    result = PhraseJudgmentBackfillResult(
+        books_seen=books_seen,
+        books_judged=sum(1 for outcome in outcomes if outcome.committed),
+        books_failed=sum(1 for outcome in outcomes if outcome.failed),
+        candidates_judged=sum(outcome.judged for outcome in outcomes),
+        protected_phrases=sum(outcome.protected for outcome in outcomes),
+        phrase_mentions=sum(outcome.mentions for outcome in outcomes),
+    )
+    logger.info(
+        "ebook_candidate_phrase_judgment_complete books_seen=%s books_judged=%s books_failed=%s "
+        "candidates_judged=%s protected=%s mentions=%s",
+        result.books_seen,
+        result.books_judged,
+        result.books_failed,
+        result.candidates_judged,
+        result.protected_phrases,
+        result.phrase_mentions,
+    )
+    return result
+
+
+def judge_book_for_backfill(
+    session: Session,
+    source_id: int,
+    config: EbookSearchConfig,
+) -> BookJudgmentResult:
+    """Judge one book's candidates and return its outcome.
+
+    Args:
+        session (Session): Active database session.
+        source_id (int): Book to judge candidates for.
+        config (EbookSearchConfig): Runtime phrase-tuning settings.
+
+    Returns:
+        BookJudgmentResult: The book's judgment outcome, or an empty result when nothing was unjudged.
+    """
+    unjudged_count = count_unjudged_candidates(session, source_id, config)
+    if not unjudged_count:
+        logger.info(
+            "ebook_candidate_phrase_judgment_book_skip_no_unjudged source_id=%s",
+            source_id,
+        )
+        return BookJudgmentResult()
+    logger.info(
+        "ebook_candidate_phrase_judgment_book_start source_id=%s unjudged=%s",
+        source_id,
+        unjudged_count,
+    )
+    return run_book_judgment(session, source_id, unjudged_count, config)
+
+
+def run_book_judgment(
+    session: Session,
+    source_id: int,
+    unjudged_count: int,
+    config: EbookSearchConfig,
+) -> BookJudgmentResult:
+    """Judge and index one book's candidates, managing its own transaction.
+
+    Commits on success and rolls back on error, returning a :class:`BookJudgmentResult`
+    that describes the outcome rather than raising it to the caller.
+
+    Args:
+        session (Session): Active database session.
+        source_id (int): Book to judge candidates for.
+        unjudged_count (int): Storable unjudged candidates counted before judging, for logging.
+        config (EbookSearchConfig): Runtime phrase-tuning settings.
+
+    Returns:
+        BookJudgmentResult: Judged, protected, and mention counts with commit and failure flags.
+    """
+    book_started_at = perf_counter()
+    try:
+        book_text = load_book_text(session, source_id)
+        if not book_text:
+            logger.warning("ebook_candidate_phrase_judgment_book_empty source_id=%s", source_id)
+            return BookJudgmentResult()
+
+        judged, protected = judge_candidate_phrases_for_book(
+            session,
+            source_id,
+            series_id=None,
+            normalized_book_text=normalize_text(book_text),
+            config=config,
+        )
+        if judged == 0:
+            logger.info(
+                "ebook_candidate_phrase_judgment_book_skip_no_judgments source_id=%s unjudged=%s",
+                source_id,
+                unjudged_count,
+            )
+            return BookJudgmentResult()
+
+        mentions = index_chunk_phrase_mentions_for_book(session, source_id, config) if protected else 0
+        session.commit()
+    except Exception:
+        session.rollback()
+        logger.exception("ebook_candidate_phrase_judgment_book_failed source_id=%s", source_id)
+        return BookJudgmentResult(failed=True)
+
+    logger.info(
+        "ebook_candidate_phrase_judgment_book_committed source_id=%s judged=%s protected=%s mentions=%s "
+        "duration_ms=%.1f",
+        source_id,
+        judged,
+        len(protected),
+        mentions,
+        (perf_counter() - book_started_at) * 1000,
+    )
+    return BookJudgmentResult(judged=judged, protected=len(protected), mentions=mentions, committed=True)
+
+
+def judge_candidate_phrases_for_book(
+    session: Session,
+    book_id: int,
+    series_id: int | None,
+    normalized_book_text: str,
+    config: EbookSearchConfig,
+) -> tuple[int, list[EbookProtectedPhrase]]:
+    """Judge unjudged candidate phrase rows for one book.
+
+    Args:
+        session (Session): Active database session.
+        book_id (int): Book whose candidates are judged.
+        series_id (int | None): Series scope for promoted protected phrases.
+        normalized_book_text (str): Whole book text, already normalized, for context lookups.
+        config (EbookSearchConfig): Runtime phrase-tuning settings.
+
+    Returns:
+        tuple[int, list[EbookProtectedPhrase]]: Number of candidates judged and the promoted phrases.
+    """
+    judgment_limit = config.protected_phrase_llm_candidates_per_book
+    if judgment_limit <= 0:
+        logger.info("ebook_candidate_phrase_judgment_skipped_llm_limit_zero book_id=%s", book_id)
+        return 0, []
+
+    existing_protected = count_protected_phrases(session, book_id)
+    target_remaining: int | None = None
+    if config.phrase_target_protected_per_book > 0:
+        target_remaining = max(config.phrase_target_protected_per_book - existing_protected, 0)
+        if target_remaining == 0:
+            logger.info(
+                "ebook_candidate_phrase_judgment_skipped_target_met book_id=%s existing_protected=%s target=%s",
+                book_id,
+                existing_protected,
+                config.phrase_target_protected_per_book,
+            )
+            return 0, []
+
+    rows = load_candidates_for_judgment(session, book_id, judgment_limit, config)
+    logger.info(
+        "ebook_candidate_phrase_judgment_candidates_loaded book_id=%s candidates=%s existing_protected=%s "
+        "target_remaining=%s judgment_limit=%s",
+        book_id,
+        len(rows),
+        existing_protected,
+        target_remaining,
+        judgment_limit,
+    )
+
+    judged_count = 0
+    protected: list[EbookProtectedPhrase] = []
+    for row_number, row in enumerate(rows, start=1):
+        candidate, judgment, candidate_row = judge_candidate_row(
+            session, book_id, series_id, normalized_book_text, row, row_number, len(rows), config
+        )
+        judged_count += 1
+        if not should_protect_judged_candidate(row, candidate, judgment, book_id, config):
+            continue
+        protected.append(upsert_protected_phrase(session, book_id, series_id, candidate, judgment, candidate_row))
+        if target_remaining is not None and len(protected) >= target_remaining:
+            break
+
+    session.flush()
+    return judged_count, protected
+
+
+def judge_candidate_row(
+    session: Session,
+    book_id: int,
+    series_id: int | None,
+    normalized_book_text: str,
+    row: EbookCandidatePhrase,
+    row_number: int,
+    total_rows: int,
+    config: EbookSearchConfig,
+) -> tuple[PhraseCandidate, LLMJudgment, EbookCandidatePhrase]:
+    """Run and persist the LLM judgment for a single candidate row.
+
+    Args:
+        session (Session): Active database session.
+        book_id (int): Book the candidate belongs to.
+        series_id (int | None): Series scope for the saved candidate.
+        normalized_book_text (str): Whole book text, already normalized, for context lookups.
+        row (EbookCandidatePhrase): Stored candidate row to judge.
+        row_number (int): 1-based position of the row in the batch, for logging.
+        total_rows (int): Total rows in the batch, for logging.
+        config (EbookSearchConfig): Runtime phrase-tuning settings.
+
+    Returns:
+        tuple[PhraseCandidate, LLMJudgment, EbookCandidatePhrase]: The candidate, its judgment,
+        and the persisted candidate row.
+    """
+    row_started_at = perf_counter()
+    candidate = phrase_candidate_from_row(row)
+    candidate.sample_contexts = row.sample_contexts or get_sample_contexts(normalized_book_text, candidate.phrase_norm)
+    logger.info(
+        "ebook_candidate_phrase_judgment_candidate_start book_id=%s candidate_id=%s row_number=%s rows=%s "
+        "phrase=%r score=%.3f raw_count=%s chapter_count=%s",
+        book_id,
+        row.id,
+        row_number,
+        total_rows,
+        candidate.phrase_norm,
+        candidate.candidate_score,
+        candidate.raw_count,
+        candidate.chapter_count,
+    )
+    judgment = judge_candidate_with_llm(candidate, config)
+    candidate_row = save_candidate_to_db(session, book_id, series_id, candidate, judgment=judgment)
+    logger.info(
+        "ebook_candidate_phrase_judgment_candidate_complete book_id=%s candidate_id=%s phrase=%r keep=%s "
+        "confidence=%.3f importance=%.3f category=%r duration_ms=%.1f",
+        book_id,
+        row.id,
+        candidate.phrase_norm,
+        judgment.keep,
+        judgment.confidence,
+        judgment.importance,
+        judgment.category,
+        (perf_counter() - row_started_at) * 1000,
+    )
+    return candidate, judgment, candidate_row
+
+
+def should_protect_judged_candidate(
+    row: EbookCandidatePhrase,
+    candidate: PhraseCandidate,
+    judgment: LLMJudgment,
+    book_id: int,
+    config: EbookSearchConfig,
+) -> bool:
+    """Report whether a judged candidate qualifies to become a protected phrase.
+
+    Args:
+        row (EbookCandidatePhrase): Stored candidate row the judgment came from.
+        candidate (PhraseCandidate): In-memory candidate that was judged.
+        judgment (LLMJudgment): Judge decision for the candidate.
+        book_id (int): Book the candidate belongs to, for logging.
+        config (EbookSearchConfig): Runtime phrase-tuning settings.
+
+    Returns:
+        bool: True when the judged candidate should be promoted to a protected phrase.
+    """
+    if not judgment.keep or judgment.confidence < config.protected_phrase_confidence_threshold:
+        return False
+    accepted_norm = normalize_text(judgment.canonical or candidate.phrase_text)
+    accepted_token_count = len(accepted_norm.split())
+    if accepted_token_count < config.phrase_min_tokens:
+        logger.info(
+            "ebook_candidate_phrase_judgment_candidate_skip_short_canonical book_id=%s candidate_id=%s "
+            "phrase=%r canonical=%r token_count=%s min_tokens=%s",
+            book_id,
+            row.id,
+            candidate.phrase_norm,
+            accepted_norm,
+            accepted_token_count,
+            config.phrase_min_tokens,
+        )
+        return False
+    if is_most_common_word_phrase(accepted_norm):
+        logger.info(
+            "ebook_candidate_phrase_judgment_candidate_skip_common_canonical book_id=%s candidate_id=%s "
+            "phrase=%r canonical=%r",
+            book_id,
+            row.id,
+            candidate.phrase_norm,
+            accepted_norm,
+        )
+        return False
+    return True
+
+
+"""LLM judging of extracted candidate phrases."""
+
+
+def judge_candidate_with_llm(candidate: PhraseCandidate, config: EbookSearchConfig) -> LLMJudgment:
+    """Ask the configured chat model to judge one pre-extracted candidate.
+
+    Args:
+        candidate (PhraseCandidate): Candidate to send to the LLM judge.
+        config (EbookSearchConfig): Runtime phrase-tuning settings and chat configuration.
+
+    Returns:
+        LLMJudgment: The parsed structured judgment for the candidate.
+    """
+    payload = {
+        "phrase": candidate.phrase_norm,
+        "token_count": candidate.token_count,
+        "sources": candidate_source_names(candidate),
+        "raw_count": candidate.raw_count,
+        "chapter_count": candidate.chapter_count,
+        "contexts": candidate.sample_contexts,
+    }
+    messages = [
+        {
+            "role": "system",
+            "content": (
+                "Judge whether a candidate phrase from a book should be protected for RAG retrieval. "
+                "Do not extract new phrases. Reject common grammar fragments, ordinary nonspecific phrases, "
+                "unstable fragments, and phrases kept only because they are frequent. Keep people, places, "
+                "organizations, factions, events, technologies, fictional conditions, magic systems, formal titles, "
+                "named concepts, and recurring world-specific terms. Return only a JSON object with keys: keep, "
+                "canonical, category, aliases, confidence, importance, allow_nested, suppress_children, reason."
+            ),
+        },
+        {"role": "user", "content": json.dumps(payload, ensure_ascii=True)},
+    ]
+    return parse_llm_judgment(request_chat_completion(config, messages), config)
+
+
+def parse_llm_judgment(content: str, config: EbookSearchConfig) -> LLMJudgment:
+    """Parse and validate an LLM phrase-judge response.
+
+    Args:
+        content (str): Raw model response text.
+        config (EbookSearchConfig): Runtime phrase-tuning settings supplying nesting defaults.
+
+    Returns:
+        LLMJudgment: The parsed and validated judgment.
+
+    Raises:
+        TypeError: If the decoded JSON body is not an object.
+    """
+    body = json.loads(extract_json_object(content))
+    if not isinstance(body, dict):
+        msg = "LLM phrase judge response is not a JSON object"
+        raise TypeError(msg)
+
+    aliases = body.get("aliases", ())
+    if not isinstance(aliases, list | tuple):
+        aliases = ()
+    return LLMJudgment(
+        keep=bool(body.get("keep", False)),
+        canonical=optional_text(body.get("canonical")),
+        category=optional_text(body.get("category")),
+        aliases=tuple(str(alias) for alias in aliases if isinstance(alias, str) and alias.strip()),
+        confidence=clamped_float(body.get("confidence"), default=0.0),
+        importance=clamped_float(body.get("importance"), default=0.5),
+        allow_nested=bool(body.get("allow_nested", config.phrase_default_allow_nested)),
+        suppress_children=bool(body.get("suppress_children", config.phrase_default_suppress_children)),
+        reason=optional_text(body.get("reason")),
+    )
+
+
+def extract_json_object(content: str) -> str:
+    """Extract a JSON object from plain or fenced model output.
+
+    Args:
+        content (str): Raw model response text.
+
+    Returns:
+        str: The substring spanning the first JSON object.
+
+    Raises:
+        ValueError: If no JSON object is found in the response.
+    """
+    stripped = content.strip()
+    if stripped.startswith("{") and stripped.endswith("}"):
+        return stripped
+    match = JSON_OBJECT_RE.search(stripped)
+    if match is None:
+        msg = "LLM phrase judge response did not contain a JSON object"
+        raise ValueError(msg)
+    return match.group(0)
+
+
+def optional_text(value: object) -> str | None:
+    """Return stripped text for a nullable JSON value.
+
+    Args:
+        value (object): Decoded JSON value that may or may not be a string.
+
+    Returns:
+        str | None: The stripped string, or ``None`` when it is not a non-empty string.
+    """
+    if not isinstance(value, str):
+        return None
+    stripped = value.strip()
+    return stripped or None
+
+
+def clamped_float(value: object, *, default: float) -> float:
+    """Coerce a JSON number into the 0.0 to 1.0 range.
+
+    Args:
+        value (object): Decoded JSON value that may or may not be a number.
+        default (float): Fallback returned when ``value`` is not numeric.
+
+    Returns:
+        float: The value clamped to ``[0.0, 1.0]``, or ``default`` when non-numeric.
+    """
+    if not isinstance(value, int | float):
+        return default
+    return min(max(float(value), 0.0), 1.0)

python/ebook_search/protected_phrases/matching.py

@@ -0,0 +1,487 @@
+"""Runtime protected-phrase matching and chunk mention indexing."""
+
+from __future__ import annotations
+
+import logging
+from collections import defaultdict
+from typing import TYPE_CHECKING
+
+from sqlalchemy import and_, delete, func, or_, select
+
+from python.ebook_search.protected_phrases.config import get_ignored_phrases
+from python.ebook_search.protected_phrases.models import (
+    ChunkPhraseHit,
+    HydratedPhraseMatch,
+    PhraseLookup,
+    PhraseMatch,
+)
+from python.ebook_search.protected_phrases.text_normalization import tokenize_with_offsets
+from python.orm.richie import (
+    EbookChunk,
+    EbookChunkPhraseMention,
+    EbookPhraseAlias,
+    EbookProtectedPhrase,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import Iterator, Sequence
+
+    from sqlalchemy.orm import Session
+
+    from python.ebook_search.config import EbookSearchConfig
+    from python.ebook_search.protected_phrases.text_normalization import NormalizedToken
+
+logger = logging.getLogger(__name__)
+
+
+def load_phrase_lookup(
+    session: Session,
+    config: EbookSearchConfig,
+    *,
+    book_id: int | None = None,
+    series_id: int | None = None,
+) -> PhraseLookup:
+    """Load protected phrases and aliases into RAM lookup maps.
+
+    Args:
+        session (Session): Active database session.
+        config (EbookSearchConfig): Runtime phrase-tuning settings.
+        book_id (int | None): Optional book scope to restrict loaded phrases.
+        series_id (int | None): Optional series scope to restrict loaded phrases.
+
+    Returns:
+        PhraseLookup: Normalized phrase and alias maps with the token-window bounds to test.
+    """
+    norm_to_ids: defaultdict[str, list[int]] = defaultdict(list)
+    alias_to_ids: defaultdict[str, list[int]] = defaultdict(list)
+    max_tokens = config.phrase_max_tokens
+
+    phrase_statement = select(
+        EbookProtectedPhrase.id,
+        EbookProtectedPhrase.phrase_norm,
+        EbookProtectedPhrase.token_count,
+    )
+    scope_filter = protected_phrase_scope_filter(book_id=book_id, series_id=series_id)
+    if scope_filter is not None:
+        phrase_statement = phrase_statement.where(scope_filter)
+
+    for row in session.execute(phrase_statement):
+        phrase_id = int(row.id)
+        phrase_norm = str(row.phrase_norm)
+        norm_to_ids[phrase_norm].append(phrase_id)
+        max_tokens = max(max_tokens, int(row.token_count))
+
+    alias_statement = select(
+        EbookPhraseAlias.alias_norm,
+        EbookPhraseAlias.phrase_id,
+    ).join(EbookProtectedPhrase, EbookProtectedPhrase.id == EbookPhraseAlias.phrase_id)
+    if scope_filter is not None:
+        alias_statement = alias_statement.where(scope_filter)
+
+    for row in session.execute(alias_statement):
+        alias_norm = str(row.alias_norm)
+        alias_to_ids[alias_norm].append(int(row.phrase_id))
+        max_tokens = max(max_tokens, len(alias_norm.split()))
+
+    return PhraseLookup(
+        norm_to_phrase_ids={key: tuple(values) for key, values in norm_to_ids.items()},
+        alias_to_phrase_ids={key: tuple(values) for key, values in alias_to_ids.items()},
+        min_tokens=config.phrase_min_tokens,
+        max_tokens=max_tokens,
+    )
+
+
+def protected_phrase_scope_filter(*, book_id: int | None, series_id: int | None) -> object | None:
+    """Build a SQLAlchemy filter for optional phrase book and series scope.
+
+    Args:
+        book_id (int | None): Optional book scope to include alongside global phrases.
+        series_id (int | None): Optional series scope to include alongside global phrases.
+
+    Returns:
+        object | None: A combined SQLAlchemy filter clause, or ``None`` when no scope is given.
+    """
+    conditions = []
+    if book_id is not None:
+        conditions.append(or_(EbookProtectedPhrase.book_id.is_(None), EbookProtectedPhrase.book_id == book_id))
+    if series_id is not None:
+        conditions.append(or_(EbookProtectedPhrase.series_id.is_(None), EbookProtectedPhrase.series_id == series_id))
+    if not conditions:
+        return None
+    return and_(*conditions)
+
+
+def generate_query_ngrams(
+    tokens_: Sequence[str],
+    min_n: int,
+    max_n: int,
+) -> Iterator[tuple[str, int, int]]:
+    """Generate normalized query windows from longest to shortest.
+
+    Args:
+        tokens_ (Sequence[str]): Normalized query tokens.
+        min_n (int): Smallest window size to yield.
+        max_n (int): Largest window size to yield, capped at the token count.
+
+    Yields:
+        tuple[str, int, int]: Normalized window text with its start and end token indices.
+    """
+    capped_max_n = min(max_n, len(tokens_))
+    for ngram_size in range(capped_max_n, min_n - 1, -1):
+        for start in range(len(tokens_) - ngram_size + 1):
+            end = start + ngram_size
+            phrase_norm = " ".join(tokens_[start:end])
+            if phrase_norm in get_ignored_phrases():
+                continue
+            yield phrase_norm, start, end
+
+
+def detect_phrase_candidates(query_text: str, lookup: PhraseLookup) -> list[PhraseMatch]:
+    """Detect protected phrase windows in a user query using RAM hash lookups.
+
+    Args:
+        query_text (str): User query text to scan.
+        lookup (PhraseLookup): In-memory phrase and alias lookup maps.
+
+    Returns:
+        list[PhraseMatch]: Unhydrated phrase matches found in the query.
+    """
+    return detect_phrase_candidates_from_tokens(tokenize_with_offsets(query_text), lookup)
+
+
+def detect_phrase_candidates_in_text(text: str, lookup: PhraseLookup) -> list[PhraseMatch]:
+    """Detect protected phrase windows in arbitrary text with character offsets.
+
+    Args:
+        text (str): Arbitrary text, such as a chunk, to scan.
+        lookup (PhraseLookup): In-memory phrase and alias lookup maps.
+
+    Returns:
+        list[PhraseMatch]: Unhydrated phrase matches found in the text.
+    """
+    return detect_phrase_candidates_from_tokens(tokenize_with_offsets(text), lookup)
+
+
+def detect_phrase_candidates_from_tokens(tokens_: Sequence[NormalizedToken], lookup: PhraseLookup) -> list[PhraseMatch]:
+    """Detect protected phrase windows from already-normalized tokens.
+
+    Args:
+        tokens_ (Sequence[NormalizedToken]): Normalized tokens with character offsets.
+        lookup (PhraseLookup): In-memory phrase and alias lookup maps.
+
+    Returns:
+        list[PhraseMatch]: Deduplicated unhydrated phrase matches with token and character spans.
+    """
+    matches: list[PhraseMatch] = []
+    seen: set[tuple[int | None, str, int, int]] = set()
+    token_texts = [token.text for token in tokens_]
+    for phrase_norm, start, end in generate_query_ngrams(token_texts, min_n=lookup.min_tokens, max_n=lookup.max_tokens):
+        phrase_ids = lookup.norm_to_phrase_ids.get(phrase_norm, ())
+        alias_ids = lookup.alias_to_phrase_ids.get(phrase_norm, ())
+        for phrase_id in (*phrase_ids, *alias_ids):
+            key = (phrase_id, phrase_norm, start, end)
+            if key in seen:
+                continue
+            seen.add(key)
+            matches.append(
+                PhraseMatch(
+                    phrase_norm=phrase_norm,
+                    phrase_id=phrase_id,
+                    start_token=start,
+                    end_token=end,
+                    token_count=end - start,
+                    start_char=tokens_[start].start_char,
+                    end_char=tokens_[end - 1].end_char,
+                )
+            )
+    return matches
+
+
+def hydrate_matches(session: Session, matches: Sequence[PhraseMatch]) -> list[HydratedPhraseMatch]:
+    """Fetch protected phrase metadata for raw phrase matches.
+
+    Args:
+        session (Session): Active database session.
+        matches (Sequence[PhraseMatch]): Unhydrated matches to enrich.
+
+    Returns:
+        list[HydratedPhraseMatch]: Matches with protected-phrase metadata attached.
+    """
+    if not matches:
+        return []
+
+    phrase_ids = sorted({match.phrase_id for match in matches if match.phrase_id is not None})
+    if not phrase_ids:
+        return []
+
+    rows = {
+        row.id: row
+        for row in session.scalars(select(EbookProtectedPhrase).where(EbookProtectedPhrase.id.in_(phrase_ids)))
+    }
+    hydrated: list[HydratedPhraseMatch] = []
+    for match in matches:
+        if match.phrase_id is None:
+            continue
+        phrase = rows.get(match.phrase_id)
+        if phrase is None:
+            continue
+        hydrated.append(
+            HydratedPhraseMatch(
+                phrase_id=phrase.id,
+                matched_norm=match.phrase_norm,
+                phrase_text=phrase.phrase_text,
+                phrase_norm=phrase.phrase_norm,
+                canonical_id=phrase.canonical_id,
+                phrase_type=phrase.phrase_type,
+                token_count=match.token_count,
+                confidence=phrase.confidence,
+                importance=phrase.importance,
+                allow_nested=phrase.allow_nested,
+                suppress_children=phrase.suppress_children,
+                start_token=match.start_token,
+                end_token=match.end_token,
+                start_char=match.start_char,
+                end_char=match.end_char,
+                book_id=phrase.book_id,
+                series_id=phrase.series_id,
+            )
+        )
+    return hydrated
+
+
+def overlaps(first: HydratedPhraseMatch, second: HydratedPhraseMatch) -> bool:
+    """Return whether two token spans overlap.
+
+    Args:
+        first (HydratedPhraseMatch): First match to compare.
+        second (HydratedPhraseMatch): Second match to compare.
+
+    Returns:
+        bool: True when the two token spans share at least one token position.
+    """
+    return not (first.end_token <= second.start_token or first.start_token >= second.end_token)
+
+
+def is_inside(child: HydratedPhraseMatch, parent: HydratedPhraseMatch) -> bool:
+    """Return whether one token span is strictly inside another.
+
+    Args:
+        child (HydratedPhraseMatch): Candidate nested match.
+        parent (HydratedPhraseMatch): Candidate enclosing match.
+
+    Returns:
+        bool: True when ``child`` lies within ``parent`` and is not the same span.
+    """
+    return (
+        child.start_token >= parent.start_token
+        and child.end_token <= parent.end_token
+        and (child.start_token, child.end_token, child.phrase_id)
+        != (parent.start_token, parent.end_token, parent.phrase_id)
+    )
+
+
+def rank_match(match: HydratedPhraseMatch) -> tuple[float, float, int]:
+    """Rank phrase matches by importance, confidence, then token count.
+
+    Args:
+        match (HydratedPhraseMatch): Match to build a sort key for.
+
+    Returns:
+        tuple[float, float, int]: A comparable key of importance, confidence, and token count.
+    """
+    return (match.importance, match.confidence, match.token_count)
+
+
+def should_suppress(candidate: HydratedPhraseMatch, kept: HydratedPhraseMatch) -> bool:
+    """Return whether an already-kept match should suppress a candidate.
+
+    Args:
+        candidate (HydratedPhraseMatch): Match being considered for keeping.
+        kept (HydratedPhraseMatch): Match already kept that may suppress the candidate.
+
+    Returns:
+        bool: True when the candidate should be dropped in favor of the kept match.
+    """
+    if not overlaps(candidate, kept):
+        return False
+    if candidate.canonical_id == kept.canonical_id:
+        return rank_match(kept) >= rank_match(candidate)
+    if is_inside(candidate, kept) and kept.suppress_children and not candidate.allow_nested:
+        return True
+    return not candidate.allow_nested and rank_match(kept) > rank_match(candidate)
+
+
+def resolve_overlaps(matches: Sequence[HydratedPhraseMatch]) -> list[HydratedPhraseMatch]:
+    """Resolve overlapping phrase matches without relying only on longest match.
+
+    Args:
+        matches (Sequence[HydratedPhraseMatch]): Hydrated matches that may overlap.
+
+    Returns:
+        list[HydratedPhraseMatch]: The kept, non-suppressed matches.
+    """
+    sorted_matches = sorted(
+        matches,
+        key=lambda match: (match.start_token, -match.token_count, -match.importance, -match.confidence),
+    )
+    kept: list[HydratedPhraseMatch] = []
+    for candidate in sorted_matches:
+        if any(should_suppress(candidate, existing) for existing in kept):
+            continue
+        kept.append(candidate)
+    return kept
+
+
+def detect_protected_phrases_for_query(
+    session: Session,
+    query_text: str,
+    config: EbookSearchConfig,
+    *,
+    lookup: PhraseLookup | None = None,
+    book_id: int | None = None,
+    series_id: int | None = None,
+) -> list[HydratedPhraseMatch]:
+    """Run the full online protected-phrase query-detection pipeline.
+
+    Args:
+        session (Session): Active database session.
+        query_text (str): User query text to detect phrases in.
+        config (EbookSearchConfig): Runtime phrase-tuning settings.
+        lookup (PhraseLookup | None): Optional preloaded lookup; loaded on demand when ``None``.
+        book_id (int | None): Optional book scope for lookup loading.
+        series_id (int | None): Optional series scope for lookup loading.
+
+    Returns:
+        list[HydratedPhraseMatch]: Hydrated, overlap-resolved phrase matches for the query.
+    """
+    active_lookup = (
+        lookup if lookup is not None else load_phrase_lookup(session, config, book_id=book_id, series_id=series_id)
+    )
+    return resolve_overlaps(hydrate_matches(session, detect_phrase_candidates(query_text, active_lookup)))
+
+
+def index_chunk_phrase_mentions_for_book(
+    session: Session,
+    book_id: int,
+    config: EbookSearchConfig,
+    *,
+    series_id: int | None = None,
+    lookup: PhraseLookup | None = None,
+) -> int:
+    """Rebuild chunk phrase mentions for all chunks in one book.
+
+    Args:
+        session (Session): Active database session.
+        book_id (int): Book whose chunk mentions are rebuilt.
+        config (EbookSearchConfig): Runtime phrase-tuning settings.
+        series_id (int | None): Optional series scope for lookup loading.
+        lookup (PhraseLookup | None): Optional preloaded lookup; loaded on demand when ``None``.
+
+    Returns:
+        int: Total number of chunk phrase mentions indexed for the book.
+    """
+    active_lookup = (
+        lookup if lookup is not None else load_phrase_lookup(session, config, book_id=book_id, series_id=series_id)
+    )
+    session.execute(delete(EbookChunkPhraseMention).where(EbookChunkPhraseMention.book_id == book_id))
+    chunks = session.scalars(select(EbookChunk).where(EbookChunk.source_id == book_id).order_by(EbookChunk.id))
+    count = 0
+    for chunk in chunks:
+        count += index_chunk_phrase_mentions(session, chunk, lookup=active_lookup)
+    session.flush()
+    logger.info("ebook_chunk_phrase_mentions_indexed book_id=%s mentions=%s", book_id, count)
+    return count
+
+
+def index_chunk_phrase_mentions(session: Session, chunk: EbookChunk, *, lookup: PhraseLookup) -> int:
+    """Store protected phrase mentions for one chunk.
+
+    Args:
+        session (Session): Active database session.
+        chunk (EbookChunk): Chunk whose text is scanned for phrase mentions.
+        lookup (PhraseLookup): In-memory phrase and alias lookup maps.
+
+    Returns:
+        int: Number of phrase mentions stored for the chunk.
+    """
+    raw_matches = detect_phrase_candidates_in_text(chunk.text, lookup)
+    hydrated = resolve_overlaps(hydrate_matches(session, raw_matches))
+    for match in hydrated:
+        session.add(
+            EbookChunkPhraseMention(
+                chunk_id=chunk.id,
+                phrase_id=match.phrase_id,
+                book_id=match.book_id if match.book_id is not None else chunk.source_id,
+                series_id=match.series_id,
+                start_char=match.start_char if match.start_char is not None else 0,
+                end_char=match.end_char,
+            )
+        )
+    return len(hydrated)
+
+
+def phrase_hits_for_chunks(
+    session: Session,
+    *,
+    chunk_ids: Sequence[int],
+    phrase_ids: Sequence[int],
+) -> dict[int, tuple[ChunkPhraseHit, ...]]:
+    """Return matched protected phrases with mention counts by chunk id using indexed chunk mentions.
+
+    Args:
+        session (Session): Active database session.
+        chunk_ids (Sequence[int]): Chunk ids to look up mentions for.
+        phrase_ids (Sequence[int]): Protected phrase ids to restrict the results to.
+
+    Returns:
+        dict[int, tuple[ChunkPhraseHit, ...]]: Phrase hits per chunk id, ordered by mention count.
+    """
+    if not chunk_ids or not phrase_ids:
+        return {}
+
+    mention_count = func.count(EbookChunkPhraseMention.phrase_id).label("mention_count")
+    statement = (
+        select(
+            EbookChunkPhraseMention.chunk_id,
+            EbookProtectedPhrase.id.label("phrase_id"),
+            EbookProtectedPhrase.phrase_text,
+            mention_count,
+        )
+        .join(EbookProtectedPhrase, EbookProtectedPhrase.id == EbookChunkPhraseMention.phrase_id)
+        .where(
+            EbookChunkPhraseMention.chunk_id.in_(chunk_ids),
+            EbookChunkPhraseMention.phrase_id.in_(phrase_ids),
+        )
+        .group_by(EbookChunkPhraseMention.chunk_id, EbookProtectedPhrase.id, EbookProtectedPhrase.phrase_text)
+        .order_by(EbookChunkPhraseMention.chunk_id, mention_count.desc(), EbookProtectedPhrase.phrase_text)
+    )
+    hits: defaultdict[int, list[ChunkPhraseHit]] = defaultdict(list)
+    for row in session.execute(statement):
+        hits[int(row.chunk_id)].append(
+            ChunkPhraseHit(
+                phrase_id=int(row.phrase_id),
+                phrase_text=str(row.phrase_text),
+                mention_count=int(row.mention_count),
+            )
+        )
+    return {chunk_id: tuple(chunk_hits) for chunk_id, chunk_hits in hits.items()}
+
+
+def phrase_hit_counts_for_chunks(
+    session: Session,
+    *,
+    chunk_ids: Sequence[int],
+    phrase_ids: Sequence[int],
+) -> dict[int, int]:
+    """Return phrase-hit counts by chunk id using indexed chunk mentions.
+
+    Args:
+        session (Session): Active database session.
+        chunk_ids (Sequence[int]): Chunk ids to count mentions for.
+        phrase_ids (Sequence[int]): Protected phrase ids to restrict the counts to.
+
+    Returns:
+        dict[int, int]: Total mention count per chunk id.
+    """
+    hits = phrase_hits_for_chunks(session, chunk_ids=chunk_ids, phrase_ids=phrase_ids)
+    return {chunk_id: sum(hit.mention_count for hit in chunk_hits) for chunk_id, chunk_hits in hits.items()}

python/ebook_search/protected_phrases/models.py

@@ -0,0 +1,262 @@
+"""Dataclasses shared by protected phrase extraction, judging, matching, and backfills."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from collections.abc import Mapping
+
+
+@dataclass(slots=True)
+class PhraseCandidate:
+    """A phrase candidate with merged extraction-source metadata.
+
+    Attributes:
+        phrase_text (str): Display text for the phrase.
+        phrase_norm (str): Normalized phrase used as the merge key.
+        token_count (int): Number of normalized tokens in the phrase.
+        source_raw_ngram (bool): Whether the raw n-gram extractor produced the phrase.
+        source_yake (bool): Whether YAKE keyword extraction produced the phrase.
+        source_spacy_ner (bool): Whether spaCy named-entity recognition produced the phrase.
+        source_spacy_noun_chunk (bool): Whether spaCy noun chunking produced the phrase.
+        source_capitalized (bool): Whether the capitalized-run extractor produced the phrase.
+        source_metadata (bool): Whether book metadata produced the phrase.
+        spacy_label (str | None): spaCy entity label when NER produced the phrase.
+        raw_count (int): Occurrences counted across the book text.
+        chapter_count (int): Number of chapters containing the phrase.
+        yake_score (float | None): Raw YAKE score when available; lower is better.
+        candidate_score (float): Combined pre-judging score.
+        sample_contexts (list[str]): Normalized context snippets around occurrences.
+    """
+
+    phrase_text: str
+    phrase_norm: str
+    token_count: int
+    source_raw_ngram: bool = False
+    source_yake: bool = False
+    source_spacy_ner: bool = False
+    source_spacy_noun_chunk: bool = False
+    source_capitalized: bool = False
+    source_metadata: bool = False
+    spacy_label: str | None = None
+    raw_count: int = 0
+    chapter_count: int = 0
+    yake_score: float | None = None
+    candidate_score: float = 0.0
+    sample_contexts: list[str] = field(default_factory=list)
+
+
+@dataclass(frozen=True, slots=True)
+class LLMJudgment:
+    """A structured phrase judgment returned by the LLM judge.
+
+    Attributes:
+        keep (bool): Whether the judge accepted the phrase for protection.
+        canonical (str | None): Canonical phrase text chosen by the judge.
+        category (str | None): Phrase category such as person, place, or event.
+        aliases (tuple[str, ...]): Alternate surface forms for the phrase.
+        confidence (float): Judge confidence between 0.0 and 1.0.
+        importance (float): Judge importance between 0.0 and 1.0.
+        allow_nested (bool): Whether the phrase may match inside a larger kept match.
+        suppress_children (bool): Whether the phrase suppresses matches nested inside it.
+        reason (str | None): Free-text explanation from the judge.
+    """
+
+    keep: bool
+    canonical: str | None
+    category: str | None
+    aliases: tuple[str, ...]
+    confidence: float
+    importance: float = 0.5
+    allow_nested: bool = False
+    suppress_children: bool = True
+    reason: str | None = None
+
+
+@dataclass(frozen=True, slots=True)
+class PhraseLookup:
+    """In-memory lookup maps used for constant-time phrase-window checks.
+
+    Attributes:
+        norm_to_phrase_ids (Mapping[str, tuple[int, ...]]): Normalized phrase to protected phrase ids.
+        alias_to_phrase_ids (Mapping[str, tuple[int, ...]]): Normalized alias to protected phrase ids.
+        min_tokens (int): Smallest token-window size to test.
+        max_tokens (int): Largest token-window size to test.
+    """
+
+    norm_to_phrase_ids: Mapping[str, tuple[int, ...]]
+    alias_to_phrase_ids: Mapping[str, tuple[int, ...]]
+    min_tokens: int
+    max_tokens: int
+
+
+@dataclass(frozen=True, slots=True)
+class PhraseMatch:
+    """An unhydrated query or chunk phrase match.
+
+    Attributes:
+        phrase_norm (str): Normalized text of the matched window.
+        start_token (int): Index of the first matched token.
+        end_token (int): Index one past the last matched token.
+        token_count (int): Number of tokens in the match.
+        phrase_id (int | None): Matched protected phrase id when known.
+        start_char (int | None): Start character offset in the source text.
+        end_char (int | None): End character offset in the source text.
+    """
+
+    phrase_norm: str
+    start_token: int
+    end_token: int
+    token_count: int
+    phrase_id: int | None = None
+    start_char: int | None = None
+    end_char: int | None = None
+
+
+@dataclass(frozen=True, slots=True)
+class HydratedPhraseMatch:
+    """A phrase match with protected-phrase metadata attached.
+
+    Attributes:
+        phrase_id (int): Protected phrase id.
+        matched_norm (str): Normalized window text that matched.
+        phrase_text (str): Display text of the protected phrase.
+        phrase_norm (str): Normalized text of the protected phrase.
+        canonical_id (str): Deterministic ``category:slug`` identifier.
+        phrase_type (str | None): Phrase category.
+        token_count (int): Number of tokens in the match.
+        confidence (float): Stored judge confidence.
+        importance (float): Stored judge importance.
+        allow_nested (bool): Whether the phrase may match inside a larger kept match.
+        suppress_children (bool): Whether the phrase suppresses matches nested inside it.
+        start_token (int): Index of the first matched token.
+        end_token (int): Index one past the last matched token.
+        start_char (int | None): Start character offset in the source text.
+        end_char (int | None): End character offset in the source text.
+        book_id (int | None): Book scope of the phrase.
+        series_id (int | None): Series scope of the phrase.
+    """
+
+    phrase_id: int
+    matched_norm: str
+    phrase_text: str
+    phrase_norm: str
+    canonical_id: str
+    phrase_type: str | None
+    token_count: int
+    confidence: float
+    importance: float
+    allow_nested: bool
+    suppress_children: bool
+    start_token: int
+    end_token: int
+    start_char: int | None = None
+    end_char: int | None = None
+    book_id: int | None = None
+    series_id: int | None = None
+
+
+@dataclass(frozen=True, slots=True)
+class ChunkPhraseHit:
+    """One protected phrase with its mention count inside one retrieved chunk.
+
+    Attributes:
+        phrase_id (int): Protected phrase id.
+        phrase_text (str): Display text of the protected phrase.
+        mention_count (int): Indexed mentions of the phrase in the chunk.
+    """
+
+    phrase_id: int
+    phrase_text: str
+    mention_count: int
+
+
+@dataclass(frozen=True, slots=True)
+class PhraseCandidateGenerationResult:
+    """Summary of candidate phrase extraction for indexed books.
+
+    Attributes:
+        books_seen (int): Indexed books examined.
+        books_built (int): Books that had candidates generated and committed.
+        candidate_phrases (int): Candidate phrases stored across all books.
+    """
+
+    books_seen: int
+    books_built: int
+    candidate_phrases: int
+
+
+@dataclass(frozen=True, slots=True)
+class PhraseJudgmentBackfillResult:
+    """Summary of LLM judging for stored candidate phrases.
+
+    Attributes:
+        books_seen (int): Indexed books examined.
+        books_judged (int): Books with judgments committed.
+        books_failed (int): Books rolled back after an error.
+        candidates_judged (int): Candidate phrases sent to the LLM judge.
+        protected_phrases (int): Protected phrases promoted from candidates.
+        phrase_mentions (int): Chunk phrase mentions indexed across all books.
+    """
+
+    books_seen: int
+    books_judged: int
+    books_failed: int
+    candidates_judged: int
+    protected_phrases: int
+    phrase_mentions: int
+
+
+@dataclass(frozen=True, slots=True)
+class BookJudgmentResult:
+    """Outcome of judging one book's candidate phrases.
+
+    Attributes:
+        judged (int): Candidate phrases sent to the LLM judge.
+        protected (int): Protected phrases promoted from candidates.
+        mentions (int): Chunk phrase mentions indexed for the book.
+        committed (bool): Whether the book's judgments were committed.
+        failed (bool): Whether the book was rolled back after an error.
+    """
+
+    judged: int = 0
+    protected: int = 0
+    mentions: int = 0
+    committed: bool = False
+    failed: bool = False
+
+
+@dataclass(frozen=True, slots=True)
+class BookCandidateResult:
+    """Outcome of generating one book's candidate phrases.
+
+    Attributes:
+        candidates (int): Candidate phrases stored for the book.
+        built (bool): Whether candidate generation was committed.
+    """
+
+    candidates: int = 0
+    built: bool = False
+
+
+@dataclass(frozen=True, slots=True)
+class PhraseRecalculationResult:
+    """Summary of phrase cleanup and candidate regeneration for one book.
+
+    Attributes:
+        book_id (int): Book the recalculation ran against.
+        deleted_candidates (int): Candidate phrase rows deleted.
+        deleted_protected_phrases (int): Protected phrase rows deleted.
+        deleted_aliases (int): Phrase alias rows deleted.
+        deleted_mentions (int): Chunk phrase mention rows deleted.
+        candidate_phrases (int): Candidate phrases regenerated after cleanup.
+    """
+
+    book_id: int
+    deleted_candidates: int
+    deleted_protected_phrases: int
+    deleted_aliases: int
+    deleted_mentions: int
+    candidate_phrases: int

python/ebook_search/protected_phrases/store.py

@@ -0,0 +1,528 @@
+"""Database persistence for candidate and protected phrase rows."""
+
+from __future__ import annotations
+
+import logging
+import re
+from typing import TYPE_CHECKING
+
+from sqlalchemy import delete, func, or_, select
+from sqlalchemy.dialects.postgresql import insert as pg_insert
+from sqlalchemy.dialects.sqlite import insert as sqlite_insert
+
+from python.ebook_search.protected_phrases.extraction import minimum_candidate_raw_count
+from python.ebook_search.protected_phrases.models import PhraseCandidate, PhraseRecalculationResult
+from python.ebook_search.protected_phrases.text_normalization import normalize_text
+from python.orm.richie import (
+    EbookCandidatePhrase,
+    EbookChunk,
+    EbookChunkPhraseMention,
+    EbookPhraseAlias,
+    EbookProtectedPhrase,
+    EbookSource,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+    from sqlalchemy.dialects.postgresql.dml import Insert as PostgresInsert
+    from sqlalchemy.dialects.sqlite.dml import Insert as SqliteInsert
+    from sqlalchemy.orm import Session
+
+    from python.ebook_search.config import EbookSearchConfig
+    from python.ebook_search.protected_phrases.models import LLMJudgment
+    from python.orm.richie.base import TableBase
+
+logger = logging.getLogger(__name__)
+
+
+def dialect_insert(session: Session, table: type[TableBase]) -> PostgresInsert | SqliteInsert:
+    """Return a dialect-specific INSERT construct that supports ``ON CONFLICT DO UPDATE``.
+
+    Production runs on PostgreSQL while tests run on SQLite; both support upserts with
+    compatible SQLAlchemy constructs, so the correct one is chosen from the bound dialect.
+
+    Args:
+        session (Session): Active database session whose bind selects the dialect.
+        table (type[TableBase]): Mapped table to insert into.
+
+    Returns:
+        PostgresInsert | SqliteInsert: A dialect insert exposing ``on_conflict_do_update``.
+    """
+    if session.get_bind().dialect.name == "sqlite":
+        return sqlite_insert(table)
+    return pg_insert(table)
+
+
+
+
+def load_book_text(session: Session, book_id: int) -> str:
+    """Load a book's indexed chunk text as one string for phrase extraction.
+
+    Args:
+        session (Session): Active database session.
+        book_id (int): Book whose chunk text is loaded.
+
+    Returns:
+        str: The book's chunk text joined into a single string.
+    """
+    texts = session.scalars(
+        select(EbookChunk.text).where(EbookChunk.source_id == book_id).order_by(EbookChunk.chunk_index)
+    )
+    return "\n\n".join(stripped for text in texts if (stripped := text.strip()))
+
+
+def load_book_chapter_texts(session: Session, book_id: int) -> list[str]:
+    """Reconstruct chapter-like text blocks from indexed chunks for phrase extraction.
+
+    Args:
+        session (Session): Active database session.
+        book_id (int): Book whose chunks are grouped into chapters.
+
+    Returns:
+        list[str]: Non-empty chapter-like text blocks in chunk order.
+    """
+    rows = session.execute(
+        select(EbookChunk.chapter_id, EbookChunk.text)
+        .where(EbookChunk.source_id == book_id)
+        .order_by(EbookChunk.chunk_index)
+    )
+    chapters: list[str] = []
+    current_chapter_id: int | None = None
+    current_parts: list[str] = []
+    have_current = False
+
+    for chapter_id, text in rows:
+        if have_current and chapter_id != current_chapter_id:
+            chapter_text = "\n\n".join(current_parts).strip()
+            if chapter_text:
+                chapters.append(chapter_text)
+            current_parts = []
+        current_chapter_id = chapter_id
+        current_parts.append(str(text))
+        have_current = True
+
+    if current_parts:
+        chapter_text = "\n\n".join(current_parts).strip()
+        if chapter_text:
+            chapters.append(chapter_text)
+    return chapters
+
+
+def metadata_for_source(source: EbookSource) -> dict[str, object | None]:
+    """Return phrase extraction metadata for one indexed source.
+
+    Args:
+        source (EbookSource): Indexed source to read metadata from.
+
+    Returns:
+        dict[str, object | None]: Title, author, language, publisher, and identifier values.
+    """
+    return {
+        "title": source.title,
+        "author": source.author,
+        "language": source.language,
+        "publisher": source.publisher,
+        "identifier": source.identifier,
+    }
+
+
+def metadata_for_source_id(session: Session, source_id: int) -> dict[str, object | None]:
+    """Return phrase extraction metadata for one indexed source by id.
+
+    Args:
+        session (Session): Active database session.
+        source_id (int): Id of the indexed source to read metadata from.
+
+    Returns:
+        dict[str, object | None]: Title, author, language, publisher, and identifier values.
+
+    Raises:
+        ValueError: If no source exists with the given id.
+    """
+    source = session.get(EbookSource, source_id)
+    if source is None:
+        msg = f"No indexed source with id {source_id}"
+        raise ValueError(msg)
+    return metadata_for_source(source)
+
+
+def count_protected_phrases(session: Session, book_id: int) -> int:
+    """Count stored protected phrases for one book.
+
+    Args:
+        session (Session): Active database session.
+        book_id (int): Book whose protected phrases are counted.
+
+    Returns:
+        int: Number of protected phrases stored for the book.
+    """
+    return session.scalars(
+        select(func.count(EbookProtectedPhrase.id)).where(EbookProtectedPhrase.book_id == book_id)
+    ).one()
+
+
+def count_unjudged_candidates(session: Session, book_id: int, config: EbookSearchConfig) -> int:
+    """Count storable candidate rows for a book that have not yet been judged.
+
+    Args:
+        session (Session): Active database session.
+        book_id (int): Book whose unjudged candidates are counted.
+        config (EbookSearchConfig): Runtime phrase-tuning settings supplying storage thresholds.
+
+    Returns:
+        int: Number of storable, unjudged candidate rows for the book.
+    """
+    return session.scalars(
+        select(func.count(EbookCandidatePhrase.id)).where(
+            EbookCandidatePhrase.book_id == book_id,
+            EbookCandidatePhrase.llm_judged.is_(False),
+            EbookCandidatePhrase.token_count >= config.phrase_min_tokens,
+            EbookCandidatePhrase.raw_count >= minimum_candidate_raw_count(config),
+        )
+    ).one()
+
+
+def load_candidates_for_judgment(
+    session: Session,
+    book_id: int,
+    judgment_limit: int,
+    config: EbookSearchConfig,
+) -> Sequence[EbookCandidatePhrase]:
+    """Load the top unjudged candidate rows for a book.
+
+    Common-word phrases are filtered before storage (``filter_storable_candidates``), so
+    no re-check is needed here.
+
+    Args:
+        session (Session): Active database session.
+        book_id (int): Book whose candidates are loaded.
+        judgment_limit (int): Maximum number of candidate rows to return.
+        config (EbookSearchConfig): Runtime phrase-tuning settings supplying storage thresholds.
+
+    Returns:
+        Sequence[EbookCandidatePhrase]: Top storable, unjudged candidate rows ordered by score.
+    """
+    return session.scalars(
+        select(EbookCandidatePhrase)
+        .where(
+            EbookCandidatePhrase.book_id == book_id,
+            EbookCandidatePhrase.llm_judged.is_(False),
+            EbookCandidatePhrase.token_count >= config.phrase_min_tokens,
+            EbookCandidatePhrase.raw_count >= minimum_candidate_raw_count(config),
+        )
+        .order_by(
+            EbookCandidatePhrase.candidate_score.desc(),
+            EbookCandidatePhrase.raw_count.desc(),
+            EbookCandidatePhrase.id,
+        )
+        .limit(judgment_limit)
+    ).all()
+
+
+def phrase_candidate_from_row(row: EbookCandidatePhrase) -> PhraseCandidate:
+    """Recreate an in-memory candidate from a persisted candidate row.
+
+    Args:
+        row (EbookCandidatePhrase): Stored candidate row to convert.
+
+    Returns:
+        PhraseCandidate: An in-memory candidate mirroring the row's fields.
+    """
+    return PhraseCandidate(
+        phrase_text=row.phrase_text,
+        phrase_norm=row.phrase_norm,
+        token_count=row.token_count,
+        source_raw_ngram=row.source_raw_ngram,
+        source_yake=row.source_yake,
+        source_spacy_ner=row.source_spacy_ner,
+        source_spacy_noun_chunk=row.source_spacy_noun_chunk,
+        source_capitalized=row.source_capitalized,
+        source_metadata=row.source_metadata,
+        spacy_label=row.spacy_label,
+        raw_count=row.raw_count,
+        chapter_count=row.chapter_count,
+        yake_score=row.yake_score,
+        candidate_score=row.candidate_score,
+        sample_contexts=row.sample_contexts or [],
+    )
+
+
+def save_candidate_to_db(
+    session: Session,
+    book_id: int,
+    series_id: int | None,
+    candidate: PhraseCandidate,
+    *,
+    judgment: LLMJudgment | None,
+) -> EbookCandidatePhrase:
+    """Insert or update one candidate phrase row.
+
+    Args:
+        session (Session): Active database session.
+        book_id (int): Book the candidate belongs to.
+        series_id (int | None): Series scope stored on the row.
+        candidate (PhraseCandidate): Candidate whose fields are written to the row.
+        judgment (LLMJudgment | None): Judgment to record, or ``None`` to leave the row unjudged.
+
+    Returns:
+        EbookCandidatePhrase: The inserted or updated candidate row.
+    """
+    values: dict[str, object] = {
+        "book_id": book_id,
+        "phrase_norm": candidate.phrase_norm,
+        "series_id": series_id,
+        "phrase_text": candidate.phrase_text,
+        "token_count": candidate.token_count,
+        "source_raw_ngram": candidate.source_raw_ngram,
+        "source_yake": candidate.source_yake,
+        "source_spacy_ner": candidate.source_spacy_ner,
+        "source_spacy_noun_chunk": candidate.source_spacy_noun_chunk,
+        "source_capitalized": candidate.source_capitalized,
+        "source_metadata": candidate.source_metadata,
+        "spacy_label": candidate.spacy_label,
+        "raw_count": candidate.raw_count,
+        "chapter_count": candidate.chapter_count,
+        "yake_score": candidate.yake_score,
+        "candidate_score": candidate.candidate_score,
+        "llm_judged": judgment is not None,
+    }
+    if candidate.sample_contexts:
+        values["sample_contexts"] = list(candidate.sample_contexts)
+    if judgment is not None:
+        values.update(
+            llm_keep=judgment.keep,
+            llm_confidence=judgment.confidence,
+            llm_category=judgment.category,
+            llm_reason=judgment.reason,
+        )
+
+    # Preserve an existing judgment when this call is only refreshing candidate fields.
+    skip_update = {"book_id", "phrase_norm"}
+    if judgment is None:
+        skip_update.add("llm_judged")
+    insert_statement = dialect_insert(session, EbookCandidatePhrase).values(**values)
+    statement = insert_statement.on_conflict_do_update(
+        index_elements=["book_id", "phrase_norm"],
+        set_={column: insert_statement.excluded[column] for column in values if column not in skip_update},
+    ).returning(EbookCandidatePhrase)
+    return session.scalars(statement, execution_options={"populate_existing": True}).one()
+
+
+def upsert_protected_phrase(
+    session: Session,
+    book_id: int,
+    series_id: int | None,
+    candidate: PhraseCandidate,
+    judgment: LLMJudgment,
+    source_candidate: EbookCandidatePhrase,
+) -> EbookProtectedPhrase:
+    """Insert or update one accepted protected phrase and its aliases.
+
+    Args:
+        session (Session): Active database session.
+        book_id (int): Book the protected phrase belongs to.
+        series_id (int | None): Series scope stored on the phrase.
+        candidate (PhraseCandidate): Candidate the phrase was promoted from.
+        judgment (LLMJudgment): Accepted judgment supplying canonical text, category, and aliases.
+        source_candidate (EbookCandidatePhrase): Candidate row the phrase was promoted from.
+
+    Returns:
+        EbookProtectedPhrase: The inserted or updated protected phrase row.
+
+    Raises:
+        ValueError: If the chosen phrase text normalizes to empty.
+    """
+    phrase_text = judgment.canonical or candidate.phrase_text
+    phrase_norm = normalize_text(phrase_text)
+    if not phrase_norm:
+        msg = f"Protected phrase normalized to empty text: {phrase_text!r}"
+        raise ValueError(msg)
+
+    values = {
+        "book_id": book_id,
+        "phrase_norm": phrase_norm,
+        "series_id": series_id,
+        "phrase_text": phrase_text,
+        "canonical_id": make_canonical_id(judgment, phrase_norm),
+        "phrase_type": judgment.category,
+        "token_count": len(phrase_norm.split()),
+        "confidence": judgment.confidence,
+        "importance": judgment.importance,
+        "allow_nested": judgment.allow_nested,
+        "suppress_children": judgment.suppress_children,
+        "source_candidate_id": source_candidate.id,
+    }
+    insert_statement = dialect_insert(session, EbookProtectedPhrase).values(**values)
+    statement = insert_statement.on_conflict_do_update(
+        index_elements=["book_id", "phrase_norm"],
+        set_={
+            column: insert_statement.excluded[column] for column in values if column not in {"book_id", "phrase_norm"}
+        },
+    ).returning(EbookProtectedPhrase)
+    row = session.scalars(statement, execution_options={"populate_existing": True}).one()
+
+    for alias_text in judgment.aliases:
+        upsert_phrase_alias(session, row, alias_text)
+    return row
+
+
+def upsert_phrase_alias(session: Session, phrase: EbookProtectedPhrase, alias_text: str) -> EbookPhraseAlias | None:
+    """Insert or update one protected phrase alias.
+
+    Args:
+        session (Session): Active database session.
+        phrase (EbookProtectedPhrase): Protected phrase the alias points to.
+        alias_text (str): Alias surface form to store.
+
+    Returns:
+        EbookPhraseAlias | None: The alias row, or ``None`` when the alias is empty or equals the phrase.
+    """
+    alias_norm = normalize_text(alias_text)
+    if not alias_norm or alias_norm == phrase.phrase_norm:
+        return None
+
+    insert_statement = dialect_insert(session, EbookPhraseAlias).values(
+        phrase_id=phrase.id,
+        alias_norm=alias_norm,
+        alias_text=alias_text,
+        confidence=1.0,
+    )
+    statement = insert_statement.on_conflict_do_update(
+        index_elements=["phrase_id", "alias_norm"],
+        set_={
+            "alias_text": insert_statement.excluded.alias_text,
+            "confidence": insert_statement.excluded.confidence,
+        },
+    ).returning(EbookPhraseAlias)
+    return session.scalars(statement, execution_options={"populate_existing": True}).one()
+
+
+def make_canonical_id(judgment: LLMJudgment, phrase_norm: str) -> str:
+    """Create a deterministic canonical id from a judgment category and phrase.
+
+    Args:
+        judgment (LLMJudgment): Judgment supplying the phrase category.
+        phrase_norm (str): Normalized phrase text to slugify.
+
+    Returns:
+        str: A ``category:slug`` canonical identifier.
+    """
+    category = slugify_identifier(judgment.category or "phrase")
+    phrase_slug = slugify_identifier(phrase_norm)
+    return f"{category}:{phrase_slug}"
+
+
+def slugify_identifier(value: str) -> str:
+    """Normalize text for use inside a canonical id.
+
+    Args:
+        value (str): Text to slugify.
+
+    Returns:
+        str: A lowercase underscore slug, or ``"unknown"`` when empty.
+    """
+    slug = re.sub(r"[^a-z0-9]+", "_", normalize_text(value).replace("'", ""))
+    return slug.strip("_") or "unknown"
+
+
+def prune_unstorable_unjudged_candidate_phrases(
+    session: Session,
+    book_id: int,
+    config: EbookSearchConfig,
+) -> int:
+    """Delete old unjudged candidate rows that no longer satisfy storage filters.
+
+    Args:
+        session (Session): Active database session.
+        book_id (int): Book whose stale candidates are pruned.
+        config (EbookSearchConfig): Runtime phrase-tuning settings supplying storage thresholds.
+
+    Returns:
+        int: Number of candidate rows deleted.
+    """
+    deleted = rowcount(
+        session.execute(
+            delete(EbookCandidatePhrase).where(
+                EbookCandidatePhrase.book_id == book_id,
+                EbookCandidatePhrase.llm_judged.is_(False),
+                or_(
+                    EbookCandidatePhrase.token_count < config.phrase_min_tokens,
+                    EbookCandidatePhrase.raw_count < minimum_candidate_raw_count(config),
+                ),
+            )
+        )
+    )
+    if deleted:
+        logger.info(
+            "ebook_candidate_phrase_unstorable_pruned book_id=%s deleted=%s min_tokens=%s min_uses=%s",
+            book_id,
+            deleted,
+            config.phrase_min_tokens,
+            minimum_candidate_raw_count(config),
+        )
+    return deleted
+
+
+def delete_phrase_data_for_book(session: Session, book_id: int) -> PhraseRecalculationResult:
+    """Delete all candidate, protected, alias, and mention phrase data for one book.
+
+    Args:
+        session (Session): Active database session.
+        book_id (int): Book whose phrase data is deleted.
+
+    Returns:
+        PhraseRecalculationResult: Deleted-row counts with ``candidate_phrases`` set to 0.
+    """
+    protected_ids = session.scalars(
+        select(EbookProtectedPhrase.id).where(EbookProtectedPhrase.book_id == book_id)
+    ).all()
+    deleted_aliases = 0
+    if protected_ids:
+        deleted_aliases = rowcount(
+            session.execute(delete(EbookPhraseAlias).where(EbookPhraseAlias.phrase_id.in_(protected_ids)))
+        )
+
+    deleted_mentions = rowcount(
+        session.execute(delete(EbookChunkPhraseMention).where(EbookChunkPhraseMention.book_id == book_id))
+    )
+    if protected_ids:
+        deleted_mentions += rowcount(
+            session.execute(delete(EbookChunkPhraseMention).where(EbookChunkPhraseMention.phrase_id.in_(protected_ids)))
+        )
+
+    deleted_protected = rowcount(
+        session.execute(delete(EbookProtectedPhrase).where(EbookProtectedPhrase.book_id == book_id))
+    )
+    deleted_candidates = rowcount(
+        session.execute(delete(EbookCandidatePhrase).where(EbookCandidatePhrase.book_id == book_id))
+    )
+    session.flush()
+    logger.info(
+        "ebook_candidate_phrase_data_deleted book_id=%s candidates=%s protected=%s aliases=%s mentions=%s",
+        book_id,
+        deleted_candidates,
+        deleted_protected,
+        deleted_aliases,
+        deleted_mentions,
+    )
+    return PhraseRecalculationResult(
+        book_id=book_id,
+        deleted_candidates=deleted_candidates,
+        deleted_protected_phrases=deleted_protected,
+        deleted_aliases=deleted_aliases,
+        deleted_mentions=deleted_mentions,
+        candidate_phrases=0,
+    )
+
+def rowcount(result: object) -> int:
+    """Return a safe integer rowcount from a SQLAlchemy execution result.
+
+    Args:
+        result (object): SQLAlchemy execution result that may expose ``rowcount``.
+
+    Returns:
+        int: The result's rowcount, or 0 when it is missing or negative.
+    """
+    count = getattr(result, "rowcount", 0)
+    return int(count if count is not None and count >= 0 else 0)

python/ebook_search/protected_phrases/text_normalization.py

@@ -2,10 +2,10 @@
 
 from __future__ import annotations
 
-import logging
+import re
 from dataclasses import dataclass
 
-logger = logging.getLogger(__name__)
+JSON_OBJECT_RE = re.compile(r"\{.*\}", re.DOTALL)
 
 
 @dataclass(frozen=True, slots=True)
@@ -18,17 +18,38 @@ class NormalizedToken:
 
 
 def normalize_text(text: str) -> str:
-    """Normalize text for phrase storage and lookup."""
+    """Normalize text for phrase storage and lookup.
+
+    Args:
+        text (str): Raw text to normalize.
+
+    Returns:
+        str: Normalized tokens joined by single spaces.
+    """
     return " ".join(token.text for token in tokenize_with_offsets(text))
 
 
 def tokenize(text: str) -> list[str]:
-    """Normalize and split text into phrase-detection tokens."""
+    """Normalize and split text into phrase-detection tokens.
+
+    Args:
+        text (str): Raw text to tokenize.
+
+    Returns:
+        list[str]: Normalized token strings.
+    """
     return [token.text for token in tokenize_with_offsets(text)]
 
 
 def tokenize_with_offsets(text: str) -> list[NormalizedToken]:
-    """Normalize text into tokens while preserving original character offsets."""
+    """Normalize text into tokens while preserving original character offsets.
+
+    Args:
+        text (str): Raw text to tokenize.
+
+    Returns:
+        list[NormalizedToken]: Normalized tokens with their source character spans.
+    """
     tokens: list[NormalizedToken] = []
     current: list[str] = []
     start_char: int | None = None
@@ -51,7 +72,14 @@ def tokenize_with_offsets(text: str) -> list[NormalizedToken]:
 
 
 def normalize_char(char: str) -> str:
-    """Normalize one character into a token character or a separator."""
+    """Normalize one character into a token character or a separator.
+
+    Args:
+        char (str): Single source character to normalize.
+
+    Returns:
+        str: The normalized token character, or a space acting as a separator.
+    """
     if char in {"\u2019", "\u2018"}:
         return "'"
     if char in {"-", "\u2013", "\u2014"}:

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 (
+from python.ebook_search.protected_phrases.matching import (
-    HydratedPhraseMatch,
     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__)