- 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.
Add a self-contained docker/ package for running the ebook search app
against the existing Postgres database on jeeves:
- Dockerfile: python:3.14-slim image, non-root user, runs the FastAPI
app on port 8070
- docker-compose.yml: service definition with library volume mount,
BM25 index volume, .env loading, and a /health healthcheck
- containers.py: Typer CLI (ebook-search-containers) for build/start/
stop/restart/logs/ps lifecycle management
- README.md: usage and configuration docs
Expose the protected phrase extraction pipeline through the web UI:
- Admin routes: POST /admin/build-phrases, /admin/generate-ngrams, and
/admin/judge-ngrams, each wrapping the protected_phrases.lib backfill
helpers, committing on success, rolling back and rendering an error
partial on failure, and reporting per-book/candidate/mention counts.
- Book detail page: show candidate, judged, and protected phrase counts,
list top candidate n-grams (with kept/rejected status) and protected
phrases, and add a POST /books/{id}/recalculate-phrases action that
clears and regenerates candidates, then redirects back with a status
message.
- Admin template: add Generate/Judge n-gram buttons.
Also reflows admin.html to 2-space HTML formatting.
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.
load_gold_queries now skips blank and `//` comment lines so the committed
section separator in queries.jsonl no longer breaks dataset/load-test loading.
Update tests left stale by the search refactor (6bc3011):
- pass the now-required rank_constant to reciprocal_rank_fusion
- expect bm25_candidates to receive the full query and drop the removed
"BM25 query preparation" timing step
- assert reranking is enabled by default
Add a Typer CLI script that drives POST /search on a running server at a
configurable concurrency and reports latency percentiles (p50/p90/p95/p99),
throughput, and HTTP status distribution. Queries are drawn from the shared
eval JSONL set so load testing and evaluation exercise the same questions.
Richie