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>
Wire the bot's AI chat pipeline (ai_functions.handle_response) to talk to
either OpenAI or the Anthropic Messages API, chosen by one active-config
switch. Behaviour on the default "gpt" config is unchanged.
constants.py:
* guarded `import anthropic` + CLAUDECLIENT (mirrors OPENAICLIENT), netrc
machine 'anthropic' / ANTHROPIC_API_KEY;
* CLAUDE_LATEST_MODEL / CLAUDE_CHEAP_MODEL (opus-4-8 / haiku-4-5);
* AI_CONFIGS + DEFAULT_AI_CONFIG loaded from an optional 3rd element of
system_gpt_settings.json (backward compatible - a 2-element file falls
back to built-in defaults, active "gpt"). Single switch: CONJURER_AI_CONFIG
env > settings "active" > "gpt".
ai_functions.py:
* provider_generate() dispatches to OpenAI (unchanged openai_call) or the new
_anthropic_call() (splits system out, alternating messages, max_tokens,
temperature omitted - Opus 4.8 rejects sampling params);
* AIError normalises both SDKs' exceptions into one category set so
handle_response keeps its single set of in-character error replies;
* select_model() reads the active config; legacy "gpt-4o" default auto-maps
to the active provider's model so the switch actually changes the backend;
* set_active_ai_config()/list_ai_configs() with best-effort persistence back
into system_gpt_settings.json index 2.
ai_commands.py:
* $gadaj_teraz <config> hybrid command (Vykidailo-gated) switches backend at
runtime;
* graceful guards when OPENAICLIENT is None: personal assistants (OpenAI
Assistants API) and DALL-E image gen degrade instead of crashing, so a
Claude-only deployment boots.
system_gpt_settings.json: add the configs block (gpt/claude/_template) as the
collection point for future backends. requirements_bot.txt: add anthropic.
bot.env.example: ANTHROPIC_API_KEY + CONJURER_AI_CONFIG. Unit tests cover the
message splitter, model selection, config listing, and error mapping.
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