opensearch-query-reviewlisted

# OpenSearch Query Building Review (Query Review) Grounding principles are in `reference/principles.md`. For facts, prefer `reference/kb/` (official docs). ## Review order (proceed exactly like this) 1. **What the query is asking** — result set, filter selectivity, frequency, sort/paging pattern. 2. **Dynamic bool** check — `reference/kb/query-dsl-basics.md` (filter context, term/match, builder safety). 3. **Relevance·paging** — `reference/kb/relevance-scoring.md` (BM25/boost/min_score) · `reference/kb/pagination-search-after.md`. 4. **Vector/hybrid** check — `reference/kb/vector-hybrid-query.md` (k vs ef_search, efficient filtering, normalization/RRF). **If hybrid/neural grep returns 0, state "not applicable"**. 5. **Slowness diagnosis** — `reference/kb/profiling-explain.md` (Profile API). Provide a fix (query diff) with severity + re-measurement (recall@k·p99). ## Quick checklist - [ ] Is a no-score condition in `must`? → `filter`/`must_not` (skip scoring + cache). - [ ] Does the dynamic builder enforce a field whitelist, a `terms` size cap, and avoid `script`/wildcard? - [ ] Is deep paging using `from`+`size`? → `search_after`/PIT. - [ ] In k-NN, is `ef_search ≥ k`? A variable `k` with a fixed `ef_search` means a ceiling mismatch. - [ ] When a filter is present, is it efficient k-NN filtering, and does a selective filter under-fetch (over-fetch/exact fallback)? - [ ] (hybrid) Is there a normalization/RRF processor? Are the neural ingest↔query model_id the same? - [ ] Di