blas_cosine_batch and accelerate_cosine_batch_vecs re-flattened the
entire Vec<Vec<f32>> corpus into a fresh Vec<f32> on every single
query before running the batch matmul — an O(N·dim) copy paid per
query when fast-math/accelerate/openblas is enabled, even though a
flat fast-path (blas_cosine_batch_flat / accelerate_cosine_batch)
already existed for pre-flattened input.
Add MemoryCache::embeddings_flat, a contiguous [N × embedding_dim]
buffer maintained incrementally in push/update/compact (O(1) amortized
append, O(dim) in-place overwrite, O(n) rebuild only on compact/bulk
load). schema.rs's direct-push load path calls the new rebuild_flat()
explicitly. flat_embeddings() now just clones the already-maintained
buffer instead of rebuilding it.
Thread the flat buffer through strategy::search_with_metrics as a new
vectors_flat parameter, used only by the Blas/Accelerate arms (now
calling the *_flat variants); other strategies are unaffected. No
current caller wires search_with_metrics into the production query
path yet (only its own tests exercise it) — this fixes the identified
per-query re-flatten and makes the flat buffer available for whenever
that wiring lands.
INT-16
ClawHDF5 Coding Agent