fix: share scans across batched vector queries (#3805)

<!-- lance-gatekeeper-fix:v1 agent=d30696bc46eb32f04c9927b0792e35d3
generation=1 -->

## Summary

- use the Lance native batch KNN path so fixed-size batch vector
searches share one flat table scan
- validate consistent query-vector dimensions and retain the per-vector
plan when offsets require its existing semantics
- add Rust and Python regressions and update Rust, Python, and
TypeScript API documentation

## Root cause

LanceDB expanded every vector in a batch into a separate scan plan and
joined the plans with `UnionExec`. For unindexed tables on S3, a batch
of ten vectors therefore ran ten concurrent full scans, amplifying CPU
and retained data enough to produce the reported memory spike.

The native Lance batch KNN path performs bounded-memory selection for
all query vectors over one flat scan. LanceDB now supplies the vectors
as a batch and avoids applying a global scanner limit to the combined
per-query results. Batch queries with a nonzero offset keep the previous
plan because the native batch API does not support per-query offsets.

## Validation

- targeted Rust batch-query plan and execution tests
- `cargo check --quiet --features remote --tests --examples`
- `cargo clippy --quiet --features remote --tests --examples`
- `cargo fmt --all -- --check`
- targeted Python batch-vector regression after rebuilding the extension
- Ruff formatting/checks for the touched Python files
- Node.js build, lint, docs generation, and targeted batch-vector Jest
test
- `git diff --check`

Fixes #2468

---------

Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
Co-authored-by: Xuanwo <github@xuanwo.io>

nodejs/lancedb/query.ts

@@ -727,11 +727,11 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
    * Add a query vector to the search
    *
    * This method can be called multiple times to add multiple query vectors
-   * to the search. If multiple query vectors are added, then they will be searched
-   * in parallel, and the results will be concatenated. A column called `query_index`
-   * will be added to indicate the index of the query vector that produced the result.
-   *
-   * Performance wise, this is equivalent to running multiple queries concurrently.
+   * to the search. A column called `query_index` will be added to indicate the index
+   * of the query vector that produced the result. Flat searches share one table scan
+   * across the query vectors, avoiding the scan and memory amplification of running
+   * multiple queries concurrently. Indexed searches may still perform per-vector
+   * index work.
    */
   addQueryVector(vector: IntoVector): VectorQuery {
     if (vector instanceof Promise) {