Store answer: reason over structure+summaries, then full section content

Rework the store map to the Vectorless primitive the collection query
should use — reason over each document's llms.txt-style structure +
summaries, select the relevant sections, pull their FULL content, then
generate:

  1. per document (parallel): load the tree, render its section outline
     (title + one-line summary per section), and ask the LLM which
     sections' full text are relevant — reasoning over summaries, not raw
     pages, and not chunks.
  2. fetch the FULL content of every selected section (a selected heading
     pulls its subsection's leaves).
  3. one generation call over the full content of ALL relevant sections
     → a single answer citing sections + documents with [n] markers.

Two cheap selection calls over compact summaries + one generation call,
instead of N page-based tree-walks. No chunking, no embeddings, no
truncated snippets — full section content drives the answer.

Controls: max_docs (0 = all), max_sections (0 = all relevant), plus a
total content budget so a large selection still fits the model context.
Citations are per section, carrying document + section title + page span
+ a quote. Returns 501 without an LLM.

Author: hallelx2