`optimize_query` only applies to semantic search, consider supporting keyword and hybrid modes

Current Behavior

optimize_query is currently only applied when search_mode == "semantic". For keyword and hybrid modes, it is silently ignored.

The gating logic is in long_term_memory.py:

# Optimize query only for semantic search; keyword and hybrid modes should
# preserve the literal text for lexical matching.
if optimize_query and text and search_mode == SearchModeEnum.SEMANTIC:
    search_query = await optimize_query_for_vector_search(text)

The current optimization uses an LLM to rewrite conversational queries into embedding-friendly form:

• "tell me what I like to eat" → "user food preferences dietary likes dislikes favorite meals cuisine"

This is effective for vector similarity but would hurt BM25/keyword matching since the rewritten terms may not appear in stored text.

Why This Matters

• Users setting optimize_query=True with search_mode="keyword" or "hybrid" get no optimization with no warning
• Hybrid mode uses both vector and keyword components — the vector half could benefit from optimization while the keyword half uses the original text

Options to Consider

1. Do nothing — current behavior is intentional and documented (docstrings now clarify this is semantic-only). Keyword search needs the original terms for lexical matching.

2. Hybrid-aware optimization — for hybrid mode, optimize the query for the vector component but pass the original text to the keyword component. This would require search_memories to accept two query strings (one optimized for embedding, one original for BM25).

3. Keyword-specific optimization — create a separate optimization path for keyword search (e.g., expanding synonyms, stripping filler words while keeping searchable terms) instead of the current vector-focused rewrite.

4. Warn when ignored — log a warning or raise when optimize_query=True is passed with a non-semantic mode, so users know it's not being applied.

Related Files

• agent_memory_server/long_term_memory.py — gating logic (line 1187)
• agent_memory_server/llm/client.py — optimize_query_for_vector_search()
• agent_memory_server/config.py — query_optimization_prompt_template