The embedding strategy uses two incompatible API call types: embedding
calls (text-to-vector) and query expansion (chat completion). Previously
both used a single embedding_llm_config, so setting an embedding model
broke query expansion and vice versa.
Add query_llm_config to AdaptiveConfig and EmbeddingStrategy so users
can specify separate models for each call type. Fallback chain preserves
backward compatibility: query_llm_config -> llm_config -> hardcoded defaults.
Also fixes base_url and backoff params not being passed to
perform_completion_with_backoff in query expansion, and simplifies
_embedding_llm_config_dict to use LLMConfig.to_dict() (which includes
the 3 backoff fields the manual extraction was missing).
Inspired by PR #1683 from @sthakrar — thank you for identifying the
issue and proposing the initial approach.
a4cc0a9f04