Move protected phrase detection into the retrieval gather so it runs
concurrently with vector and BM25 candidates instead of sequentially
before them. Make the search API accept real bool form fields for
rerank/phrase_matching, gate phrase matching on both the request and
config kill switch, and reflow log f-strings for readability.
Run full-book candidate generation inside worker-owned sessions so each book commits independently during backfills. Abort recalculation when a book has no indexed chapters to preserve existing phrase data, and update admin/UI tests for the new generation flow.
Convert the ebook-search web app to async end to end and add concurrency
to the protected-phrase extraction and judging pipeline so large books no
longer block the event loop or the UI.
ORM / infra:
- Add get_async_postgres_engine and factor shared URL/connect_args building
into build_postgres_url (reused by the sync and async engine builders)
- Add async FastAPI session helpers (get_async_db, AsyncDbSession) with
expire_on_commit=False to avoid implicit IO under asyncio
App:
- Use AsyncEngine/AsyncSession throughout routes, search, ingest, embeddings,
answer, rerank and LLM calls; convert handlers to async
- Share a single httpx.AsyncClient in app state for LLM requests; size the
connection pool for concurrent phrase-judging workers
- Add judge_tasks: run per-book judging as tracked background tasks so a
book already being judged isn't double-queued
Protected phrases:
- Add a process pool (pool.py) and worker-count config
(extraction/judge book/phrase workers) to parallelize candidate generation
and judging
- Split admin actions into all/missing variants for generation and judging
Config:
- Add protected_phrase_extraction_workers, phrase_judge_book_workers,
phrase_judge_phrase_workers
- 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.
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.
Richie