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>
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>
gitea