fix(moa): call reference + aggregator models through their provider's real route (#53580)

MoA was calling reference and aggregator models through a bare
call_llm(provider=slot["provider"], model=slot["model"]) with a forced
temperature and a forced max_tokens (the preset's hardcoded 4096). That left
base_url/api_key/api_mode unresolved — so the auxiliary auto-detector guessed
the API surface instead of using the provider's real runtime, and the 4096 cap
truncated long aggregator syntheses.

A MoA slot is just a model selection and must be called the same way any model
is called elsewhere. Each slot is now resolved through resolve_runtime_provider
(the canonical provider→api_mode/base_url/api_key resolver the CLI, gateway, and
delegate_task all use) via a new _slot_runtime() helper, and the resolved
endpoint is passed into call_llm. So a reference/aggregator gets its provider's
actual API surface — MiniMax → anthropic_messages, GPT-5/o-series →
max_completion_tokens, custom endpoints → their base_url — identical to how that
model is handled as the acting model.

MoA also no longer imposes its own output cap: max_tokens defaults to None
(omitted → the model's real maximum) for references and is passed through from
the caller for the aggregator. The preset's hardcoded 4096 is gone. The
max_tokens preset config field is left in place (config/web/desktop unchanged);
it is simply no longer applied as a forced cap.

Tests: slots route through resolve_runtime_provider with resolved base_url/
api_key; resolution errors fall back to bare provider/model; neither call
carries an output cap even when the preset config still contains max_tokens.
This commit is contained in:
Teknium 2026-06-27 04:39:42 -07:00 committed by GitHub
parent 3fe16e3cd5
commit 02b32e2d7c
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3 changed files with 184 additions and 16 deletions

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@ -828,7 +828,6 @@ def run_conversation(
aggregator=moa_config.get("aggregator") or {},
temperature=float(moa_config.get("reference_temperature", 0.6) or 0.6),
aggregator_temperature=float(moa_config.get("aggregator_temperature", 0.4) or 0.4),
max_tokens=int(moa_config.get("max_tokens", 4096) or 4096),
)
if _moa_context:
for _msg in reversed(api_messages):