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.
Add test_protected_phrases.py covering phrase-matching behavior in the
RAG engine, and update the existing ebook_search tests to use the async
SQLAlchemy engine/session (create_async_engine, AsyncSession) and async
HTTP paths.
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
The async engine needs greenlet at runtime, which the asyncio extra
provides. Test-only deps (aiosqlite, pytest-asyncio) stay out of the
image.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
- 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.
Introduce four ORM models and their Alembic migration to support
phrase-based query matching in the ebook RAG engine:
- EbookCandidatePhrase: high-recall phrase candidates extracted per book,
with source flags (ngram/yake/spacy/capitalized/metadata), scoring, and
LLM judge results.
- EbookProtectedPhrase: phrases accepted by the LLM judge, with canonical
id, importance, and nesting controls.
- EbookPhraseAlias: normalized aliases mapping to protected phrases.
- EbookChunkPhraseMention: precomputed phrase occurrences within chunks.
Export the new models from python.orm.richie and add a JSON_DOCUMENT
helper (JSON with JSONB postgres variant) for storing sample contexts.
Move signal_alert out of python/common.py into a dedicated
python/signal_alert.py module and update its importers
(validate_system.py, snapshot_manager.py) to the new path.
Relocate the signal_alert tests from tests/test_common.py into
tests/test_signal_alert.py, repatching python.signal_alert.logger and
python.signal_alert.Apprise to match the new module.
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