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.
- implement FastAPI and HTMX lobby and game interfaces
- support up to four human and AI-controlled players
- add configurable rules, victory conditions, and expansion modules
- support validated JSON cards, patrons, objectives, and outposts
- Deleted base template (base.html) and all related contact templates (contact_detail.html, contact_form.html, contact_list.html, graph.html, need_list.html).
- Removed partial templates for managing contacts and needs (contact_table.html, manage_needs.html, manage_relationships.html, need_items.html).
- Eliminated contact API service configuration (contact_api.nix) from the NixOS setup.
The gitea runner containers have no docker access, so build the test
env with uv from the existing lockfile and run pytest directly:
- test_ebook_search workflow: uv sync --locked + uv run pytest, with
UV_PYTHON_DOWNLOADS=never so uv uses the nix-provided python 3.14
- add uv to the runner hostPackages (needs a jeeves rebuild to apply)
- ignore nested **/.venv in .dockerignore (uv sync creates one in
python/ebook_search/docker)
- document the uv test commands in the docker README; the docker test
image remains for validating the image itself
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
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.
Replace old_installer.py with a curses TUI installer packaged as a
one-file PyInstaller binary (python/installer/build.py, wrapped by
python/installer/package.nix). The default .#installer is patched to run
on foreign Linux live media; the new .#installer-nixos variant keeps its
Nix store interpreter so it runs on NixOS.
Add systems/iso, a minimal NixOS install CD with kernel 6.18 and ZFS 2.4
matching the deployed systems and the installer on PATH; build it with
nix build .#iso. Shared logging and subprocess helpers move out of
common.py into python/logging_config.py and python/process.py.
Add a pipeline to load HAProxy `option httplog` lines into the Richie
database so bot/crawler traffic can be analyzed.
- model: HaproxyRequest mirroring the httplog format, with a unique
line_hash dedup key and indexes on common filter columns
- migration: create the haproxy_request table (unique line_hash + indexes)
- haproxy_logs package:
- parser: httplog line -> columns, strips the journald prefix and
hashes the normalized line
- ingest: batched, idempotent insert that skips rows whose line_hash
already exists, so re-ingesting the same logs is a no-op
- cli: ingest-only `haproxy-logs` command reading stdin or a file
- tests: parsing of a real GPTBot line and idempotent re-ingestion
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