The client no longer strips sampling parameters per model, so the
sanitization test and its capturing provider helper assert behavior
that was intentionally removed. Delete them to restore a green build.
Signed-off-by: Émile Ré <emile@probo.com>
The common-pattern enrichment and tracker-mapping agents run on
reasoning models such as gpt-5-nano, which reject an explicit
temperature and fail the whole request with a 400 ("Unsupported
value: 'temperature' does not support 0.1 with this model"). The
model registry already records this capability, but nothing
consulted it before dispatch, and dated provider snapshots like
gpt-5-nano-2025-08-07 did not resolve in the registry.
Resolve dated snapshots to their undated base model in registry
Lookup, and sanitize each chat completion request in the LLM
client by omitting the sampling knobs the target model does not
accept (temperature, top_p, frequency/presence penalties, stop).
Unknown models are left untouched, so models absent from the
registry keep their current behavior.
Signed-off-by: Émile Ré <emile@probo.com>
Expand mixed inline/multiline function calls so each argument
is on its own line, matching the one-argument-per-line rule.
Signed-off-by: Sacha Al Himdani <sacha@getprobo.com>
StreamAccumulator never set its model field, so
Response().Model was always empty for streamed
completions. Add a Model field to stream events
and populate it in all three providers (OpenAI,
Anthropic, Bedrock).
Signed-off-by: Bryan Frimin <bryan@getprobo.com>
