Switching to a backend whose configured model the server does not have
succeeded silently, and then every reply failed with "model not found" with
nothing explaining why. The switch already fetches the model list to show
what else is available, so use it: if the config's model is absent, say so
and list what IS there.
Found while probing the real server (192.168.1.72): it has exactly one
model, gemma4:e2b, so the built-in llama3.1:8b default would have hit this
on the first switch.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The bot could talk to OpenAI or Anthropic; this adds Ollama as a third
provider so it can run against models hosted on our own box, and extends
the switch command to pick WHICH model - not just which backend.
Provider: Ollama exposes an OpenAI-compatible /v1 surface, so the client is
just openai.AsyncOpenAI(base_url=OLLAMA_URL + "/v1"). That reuses the
existing message format and the whole _map_openai_error mapping instead of
forking a second error taxonomy. There is no API key - the endpoint IS the
configuration, so the backend stays dormant (and refuses to be selected,
with a message naming the variable) until CONJURER_OLLAMA_URL is set, the
same way the Conan bridge behaves.
Model selection:
* list_provider_models() asks the SERVER for Ollama (/v1/models), so the
picker shows what is actually pulled on the box rather than a hardcoded
list. Hosted providers just report what they are wired to.
* set_active_model() pins the config's latest_model and persists it;
cheap_model is left alone so the MUSIC path keeps its cheaper backend.
* $gadaj_teraz now takes "<config> [model]", and a new read-only $modele_ai
lists what is available. Pinning an id Ollama does not have is rejected up
front with the real list - otherwise the typo only surfaces later as a
failed reply.
Two fixes this exposed:
* AI_CONFIGS now merges built-in defaults with the settings-file block
instead of letting the file win outright. Every provider switch persists a
"configs" block, so a file written by an older build would have
permanently hidden ollama from the picker after an upgrade.
* _persist_active_ai_config assigns "configs" instead of setdefault, so a
pinned model actually survives a restart.
* the hardcoded 120s response timeout is now CONJURER_AI_TIMEOUT_SECONDS - a
self-hosted model on a modest GPU can legitimately need longer.
Tests cover: ollama appears in the picker, select_model maps the legacy
gpt-4o default instead of leaking it, model listing (server-queried, sorted,
de-duplicated, failure -> AIError, unconfigured -> auth), pinning (latest
only, blank/unknown rejected), and that provider_generate routes to the new
path. Suite: 68 unit + 70 integration green.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Seven confirmed defects from the code audit, each small and low-risk.
* ai_functions.get_random_cyclic_message: random.randint(0, len(CYCLIC_WORDS))
is inclusive -> could return len -> IndexError. Now randrange(len) + guard on
an empty CYCLIC_WORDS.
* librarian_commands.get_image_sadox: random.randrange(0, len(res)-1) never
picked the last comic and raised ValueError('empty range') on a single file.
Now randrange(len) + an empty-dir guard.
* ai_commands image generation: every DALL-E error branch replied but did not
return, so control fell through to `if response:` with response unbound ->
UnboundLocalError right after the friendly message. Each branch now returns;
response is pre-initialised; and PermissionDeniedError no longer passes a
(message, text) tuple as a single arg.
* search_bot DOI match: `item["DOI"] in data` was a substring test, so a DOI
that is a prefix of a longer one (10.1/1 vs 10.1/12) produced a false 'exists'
hit. Now matches the line's first whitespace token exactly, via an O(1) dict
index built once per consumer (also removes the O(queried-DOIs) per-line scan
- a real win for large databases).
* communication_subroutine.scan_incoming: matched records were never removed
from awaiting_q, so it grew unbounded over uptime and a reused UUID could
re-match a stale record. Matched records are now dropped after dispatch.
* communication_subroutine.id3: (resp.headers.get("icy-name") or "").title()
guards against a stream that omits headers (was AttributeError on None,
500-ing the /prepped_tracks "next" handler).
* betoniarka.scan_tracks: waits for the radio logs to exist instead of dying
with FileNotFoundError on a fresh deploy (which silently killed the
now-playing forwarder until a restart).
Verified: tests/unit/test_search_bot.py gains exact-match and trailing-metadata
cases; full unit job 43 passed. Remaining observations (image-gen stale
/home/pi fallback paths + dead FileNotFoundError-after-OSError branch; tailer
still vulnerable to mid-run log rotation; DOI-first-token assumption) noted for
follow-up - none are crashes on the normal path.
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
$wyszukaj_linki_do_dokumentow but sets ai_review=True on the QueryControl, which
rides the round-trip and is matched back by UUID. When the hits return,
check_data_q sends the raw list as before, then - if flagged - hands the same
list (already in Crossref-relevance order) plus the search phrase to the AI
queue for a weighted re-rank and per-source review, delivered to the same
channel. QueryControl gains an ai_review flag (default False, so the orphan path
and all existing callers are unaffected).
Confirmed separately (and noted in the docs): the DOI list the AI receives is
pre-sorted by Crossref relevance - the librarian pipeline only filters (drops
title-less items) and splits (in-db / not-in-db), never re-sorts, and relies on
insertion-ordered dicts (Py 3.7+).
Verified: /ai_query auth (401/200/400/open), submit_ai_query and the queued
dict shape, and the QueryControl flag - via a Flask test client and
tests/integration/test_ai_query_endpoint.py (5 tests, all pass; integration
suite 11 passed, the 3 failures are the pre-existing /clear_pr_pls musician
tests fixed on a separate branch). Full first-party compile clean.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Quality-of-life: checking which AI backend is live no longer requires
switching to it. Bare `$gadaj_teraz` replies with the active config and the
selectable ones; that read-only path is open to everyone, while switching
stays Vykidailo-gated.
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>
Make the stable 'working copy' bot the canonical code at the repository
root so the install/deploy scripts operate against it again.
- Move working_copy/* to root (bot entrypoint is bot.py)
- Restore root-level install/ops scripts from c4fa88e (deploy.sh,
install_main_bot.sh, status_report.*, conjurer.service, etc.)
- Fix deploy.sh: copy bot.py (was thin_client.py) and add the
conanjurer_* modules; bump command count
- Remove side-by-side variant dirs (backup_old_docker, prototype_one,
prototype_musician_one, musician_old, working_copy) and docker cruft
- Keep components as subdirs: conjurer_librarian, conjurer_musician,
spotify_dl, yt_dlp, fonts, utils, docs
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
gitea
Michal Tuszowski