The librarian health check was a plain GET to '/', which only proved
Flask was listening - not that the service could actually take a query,
run it through its internal queue+worker, and answer back. So the cog
could load against a librarian whose worker was wedged or that couldn't
reach the bot on the return leg.
Replace it with a ping that travels the SAME path a real search does, on
both sides:
bot: QueryControl -> OUT_COMM_Q -> scan_queue -> awaiting_q
librarian: POST /ping -> librarian_queue -> worker pulls it off
(no Crossref/DOI search) -> pongs back with the same uuid
bot: /conjurer -> incoming_q -> scan_incoming matches uuid, wakes waiter
The cog enables only when that whole loop closes within 3s. This also
proves the librarian->bot return path, which a GET never did.
Safety: uuid is random per ping; the wait and POST are both bounded so
startup can't stall; a pong that finds no waiter is dropped (never
orphaned into IN_COMM_Q, which would make the cog post a bogus 'no
results' message); and a ping whose pong never returns is swept out of
awaiting_q after PING_TTL_SECONDS so nothing leaks. All awaiting_q writes
stay within scan_queue (append) and scan_incoming (remove) - no locks,
no cross-thread mutation.
Integration tests cover: OK round-trip, timeout when accepted-but-no-pong,
unreachable, non-200, orphan-pong-dropped, and that real results still
reach IN_COMM_Q. Suite: 24 integration + 41 unit green.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
test_musician_auth.py still probed /clear_pr_pls on the musician, but that
endpoint moved to betoniarka during the split - the musician now 404s it, so
all three tests failed 404 != 401/200. This was pre-existing debt, unrelated to
the AI/share/bridge work; it just kept the integration job red.
Split the coverage to match the current architecture:
* test_musician_auth.py exercises the same auth contract (no key -> 401, key ->
200, key unset -> open) against /get_share_list, an authenticated endpoint the
musician still serves, with a valid body so the permitted case is a clean 200
rather than a 400;
* new test_betoniarka_auth.py covers /clear_pr_pls where it now lives, pointing
PRIORITY_PLAYLIST_PATH at a tmp file so the authorised case can truncate it,
and checks /ping stays open;
* conftest.py adds conjurer_betoniarka to sys.path so the service imports.
Verified in a clean venv (pytest flask waitress requests), matching the CI
integration job: 13 passed, up from 3 failed / 6 passed. Unit suite unaffected
(23 passed).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Two connected features.
1) AI query interface (via the comm layer). communication_subroutine gains an
AI_QUERY_Q, a submit_ai_query() in-process entry point, and an authed
POST /ai_query endpoint ({prompt, channel_id, request_type?, username?}). The
prompt is queued and answered asynchronously by a new tasks.loop worker in the
always-loaded AI cog (Events), which calls handle_response - so it runs on
whichever backend $gadaj_teraz currently selects (GPT or Claude) - and posts the
answer to the requested channel, chunked to Discord's limit. request_type "NONE"
(default) is a clean one-shot: no persona system prompt, no memory write. The
worker starts before the OpenAI guard in cog_load, so it also runs on a
Claude-only box; cog_unload cancels it.
2) Librarian AI review. New command $wyszukaj_z_recenzja mirrors
$wyszukaj_linki_do_dokumentow but sets ai_review=True on the QueryControl, which
rides the round-trip and is matched back by UUID. When the hits return,
check_data_q sends the raw list as before, then - if flagged - hands the same
list (already in Crossref-relevance order) plus the search phrase to the AI
queue for a weighted re-rank and per-source review, delivered to the same
channel. QueryControl gains an ai_review flag (default False, so the orphan path
and all existing callers are unaffected).
Confirmed separately (and noted in the docs): the DOI list the AI receives is
pre-sorted by Crossref relevance - the librarian pipeline only filters (drops
title-less items) and splits (in-db / not-in-db), never re-sorts, and relies on
insertion-ordered dicts (Py 3.7+).
Verified: /ai_query auth (401/200/400/open), submit_ai_query and the queued
dict shape, and the QueryControl flag - via a Flask test client and
tests/integration/test_ai_query_endpoint.py (5 tests, all pass; integration
suite 11 passed, the 3 failures are the pre-existing /clear_pr_pls musician
tests fixed on a separate branch). Full first-party compile clean.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The two scaffold workflows (Python application / Python package) failed on
every PR: they installed deps from a non-existent requirements.txt, ran
flake8/pytest over the vendored yt_dlp fork (new syntax under the 3.8/3.9
matrix), and collected ad-hoc root scripts — notably test_ai.py, which is
an invalid pasted object dump (not Python).
- Remove python-app.yml / python-package.yml and the junk root scripts
(test.py, test_ai.py, test_time.py)
- Add .github/workflows/ci.yml with three PR-check jobs:
* compile — py_compile every first-party .py (no deps)
* unit — pytest on pure logic (conanjurer_functions, constants)
* integration — boot the Flask services and assert the X-Conjurer-Api-Key
auth contract (communication_subroutine + conjurer_musician)
- Add tests/ suite, pytest.ini (testpaths=tests) and conftest.py (sys.path)
Fixes surfaced by the compile gate / needed for the integration job:
- conjurer_librarian/search_bot.py + search_bot2.py: f-string reused the
same quote ({item["exists"]}) -> SyntaxError on Python < 3.12
- conjurer_musician/media_search_functions.py: made import-safe
(env-overridable paths, lazy DB load / mkdir) so the service can be
imported and tested off the Pi
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
gitea
Michal Tuszowski