* fix(llm): forward provider_params.timeout to the OpenAI-compatible embedding client
#832 and #903 added a configurable request timeout for the LLM registry
and the Gemini embedding client respectively, but the OpenAI-compatible
embedding client (src/embedding_client.py) was never wired up. It
constructed AsyncOpenAI with no timeout at all, so a stalled socket
against a slow or contended OpenAI-compatible backend (e.g. a
self-hosted embedding model under load) wedges the deriver worker's
event loop indefinitely — the exact failure #785/#903 describe, just
via a code path #903 didn't cover.
EmbeddingModelConfig now carries provider_params through from
resolve_embedding_model_config, mirroring how resolve_model_config
already does it for ModelConfig, and the OpenAI branch of
_EmbeddingClient.__init__ extracts `timeout` via the existing
request_timeout_from_extra_params helper. Unset stays unset — no
existing behavior changes.
Reproduced and verified against a real self-hosted deployment (local
Ollama backend under load): before this fix, a single stuck embedding
call blocked all deriver queue processing for 20+ minutes with no
error logged, twice in one session.
* fix(embedding): use first-class timeout on embedding model config
provider_params is the LLM per-request escape hatch; embedding timeouts are
client-construction knobs and belong next to max_batch_size. Wire the field
for OpenAI and Gemini, omit the OpenAI kwarg when unset so the SDK default
stays, and keep Gemini's 10-minute floor when unset.
* test(embedding): live coverage for first-class embedding timeout
Exercise EmbeddingModelConfig.timeout on one representative OpenAI and
Gemini model: configured timeout lands on the SDK client, and a near-zero
timeout aborts before the provider answers.
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Co-authored-by: Aakash Kattelu <aakash@plasticlabs.ai>
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