Richie
f8b5ba82a6
Refactor protected phrase handling from a single module into a python/ebook_search/protected_phrases package covering extraction, storage, and runtime matching. Phrase filtering is now data-driven via bundled TOML files: ignored_phrases, bad_starts, bad_ends, and most_common_words. Add phrase-tuning settings to EbookSearchConfig so candidate generation, scoring, LLM judging, and matching are configurable rather than hardcoded: token bounds, entity token limit, raw n-gram min count, frequency and chapter-spread score thresholds, candidate/LLM/target caps, confidence threshold, nesting defaults, and the phrase hit boost.
6 lines
43 B
TOML
6 lines
43 B
TOML
tokens = [
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"a",
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"an",
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"the",
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]
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