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a4cc0a9f04
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.