Fox 2026-04-29: querying "who is batman" returned only ONE source
(List_of_Batman_comics — an 80 KB+ bibliography) despite top_k=8 &
the actual bio article being in the corpus. Greedy fill: hit #1
consumed the entire 60 KB budget, every subsequent doc dropped with
char_budget <= 0.
Fix in aborist/qa/query.py:
per_source_cap = max(1, max_context_chars // max(1, top_k))
for h in hits[:top_k]:
text = _load_doc_text(...)
if len(text) > per_source_cap:
text = text[:per_source_cap] # NEW: per-source cap first
if len(text) > char_budget:
text = text[:char_budget]
...
Each top_k hit gets at most max_context_chars/top_k chars (default
60K/8 = 7.5K each — plenty for a chunk or two of prose). Total
context ≤ max_context_chars by construction. top_k=1 preserves the
legacy behavior (single source can use the full budget).
End-to-end effect on Batman: the bio (Wikipedia/Batman article) lands
in context alongside List_of_Batman_comics; the model can paraphrase-
verify against the actual character introduction text instead of
fabricating from training.
Tests: 2 regressions in tests/test_query.py — multi-source delivery
when hit #1 is huge, and top_k=1 single-source still allowed full
budget. 337 passed, 1 skipped.
Burned the two stale Batman cache records (chain extended) so a
fresh `make query Q="who is batman?"` exercises the new path.
russell@unturf.com
3a9aa729c5