Adversarial review of the previous commit found a real regression it
introduced, reproduced against the actual code rather than inferred.
Changing _persist_active_ai_config from setdefault("configs", ...) to a
direct assignment made every backend switch write the whole in-memory
AI_CONFIGS over the settings file. Because AI_CONFIGS is now the built-in
defaults merged UNDER the file, that meant:
* an operator's hand edits were destroyed - and hand editing is the only
way to change cheap_model / temperature / max_tokens, since
set_active_model writes latest_model and there is no command for the rest,
* a config deliberately deleted from the file was re-seeded from the
defaults and written back, permanently,
* pinning a model for one provider silently reverted another provider's
entry,
* CONJURER_OLLAMA_MODEL stopped having any effect once the env-derived
block had been persisted once.
The original motivation was still valid (plain setdefault would drop a
pinned model), so the fix is narrower rather than a revert: persist ONLY
the field this process actually changed. _persist_active_ai_config takes
model_for and writes back just that config's latest_model; everything else
in the on-disk block is left exactly as found. The constants.py merge stays
- it is what keeps a newly added provider visible after an upgrade - and is
now in-memory only, so it cannot reach the file.
Tests: the disk-write path had ZERO coverage, which is precisely how this
got in. Added four tests that drive the real _persist_active_ai_config
against a temp settings file: the pin lands while operator edits survive and
a deleted config is not resurrected; a plain switch leaves the configs block
byte-identical; a pin survives a re-read; a corrupt file does not raise.
Verified they have teeth - reintroducing the regression fails two of them.
Also hardened two weak tests the review caught: the pin test asserted on the
object set_active_model returns, which IS the mutated dict (so it passed
regardless), and the unconfigured-endpoint test monkeypatched OLLAMACLIENT
to None when it was already None, passing vacuously.
Suite: 72 unit + 70 integration green.
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>
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>
gitea
Michal Tuszowski