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* feat(edge): add query_batch for batched planned queries Expose the planned-query batch path as a public API so multiple independent queries can share one planning pass over leaf searches and scrolls. Wired through EdgeShardRead, FFI, and Python bindings. Co-authored-by: Cursor <cursoragent@cursor.com> * perf(edge): push batched query vectors down to segments `query_batch` planned the whole batch at once but then executed every leaf search on its own: one query context, one fan-out over all segments, and one single-vector `Segment::search_batch` call per leaf. Execute the batch as a batch instead: - `EdgeReadView::search_batch` builds the query context once, visits the segments once, and hands each segment the leaves that agree on everything but their query vector as a single multi-vector `search_batch` call. `search` is now a thin wrapper over a one-element batch. - Move `SearchType`/`BatchSearchParams` from `collection`'s segments searcher into `shard`, next to `CoreSearchRequest`, and add `group_search_batches` so both the collection and the edge read path share one grouping implementation. Edge computes the grouping once and reuses it per segment. - `search_matrix` now issues its per-sample nearest queries through `query_batch`; they share filter, limit and vector name, so the whole sample is scored in one batched search per segment instead of one full segment pass per sampled point. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Cursor <cursoragent@cursor.com> Co-authored-by: generall <andrey@vasnetsov.com> Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>