* feat(llm): extra request-body fields via LLM_EXTRA_BODY
Some provider parameters, such as OpenAI's service_tier and
safety_identifier, are read only from the request body, so
LLM_EXTRA_HEADERS can't set them. LLM_EXTRA_BODY merges a JSON object
into the body of every request to the configured model, on both routing
paths. A dedicated dedupe model gets its own DEDUPE_LLM_EXTRA_BODY
instead, as with headers.
* fix(config): decode saved JSON-object settings when loading
persist_current() saves env values verbatim, so LLM_EXTRA_HEADERS,
LLM_EXTRA_BODY and their DEDUPE_ variants land in cli-config.json as JSON
text. The loader passed that text unchanged to the dict fields, so a later
launch without the env var failed validation. Decode saved strings for
dict fields, as pydantic-settings does for env vars.
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Co-authored-by: Szymon Nastaly <szymon.nastaly@autoform.ch>
preflight_request takes api_base_setting so a dedupe model that timed out
points the user at DEDUPE_LLM_API_BASE, not LLM_API_BASE. warm_up_llm is
now exercised with a dedicated dedupe model: own headers, same preflight
timeout, resolved through resolve_dedupe_model.
The warm-up request used settings.llm.timeout (LLM_TIMEOUT, 300s) both as
the request timeout and the wait_for bound, so a wrong LLM_API_BASE or a
dead proxy hung five minutes and then printed an empty Error line.
Add LlmSettings.preflight_timeout (LLM_PREFLIGHT_TIMEOUT, default 30) and
a shared preflight_request() used for the main and dedupe models. When it
expires the panel names the model, the limit and the setting.
