`expand_with_items` fetched its own inputs per document: one query to resolve
refs to positions, one for the window of items around them. A result set spanning
N documents cost 2N queries, which was 10 of the 18 measured for a limit=5 search
on a remote object-store corpus.
`expand_context` now does both fetches once for every document it is expanding,
and `expand_with_items` takes the positions and items it needs. Two queries for
one document, and two for five.
Each document keeps its own inclusive window in `get_items_in_ranges`. Positions
repeat across documents, so a shared range would splice one document's items into
another's context.
`_populate_image_data` ran its stages once per result document, so a result set
spanning N documents cost 4N `document_items` queries. Measured on a remote
object-store corpus, a limit=5 search with expansion was 18 queries, 16 of them
against `document_items`.
The stages now run once each across every document, and flat in document count:
two queries for the dependent caption-to-picture mapping when results ranked on a
caption, one for the picture bytes. Two queries for a picture-ref result set,
three at most.
Picture text comes back with the bytes rather than from a second query, since it
is on the same rows.
Predicates are per document, `(document_id = 'a' AND self_ref IN (…)) OR (…)`,
rather than `self_ref IN (union)`. self_ref and position values repeat across
documents, so a union predicate would return other documents' rows: for
picture_data that fetches blobs nobody asked for, and it can hand one document
another document's picture.
