Both halves of hybrid retrieval were reading the same 331 titles and summaries.
The lexical half was fixed earlier; this is the semantic one. `content` is NULL
for 323 articles because the generator writes into `sections`, so the vector for
98% of the library described the heading and nothing under it.
Depth is carried by the section index, where the longest section in the corpus
is under the embedding clamp — so every sentence of every body is embedded whole
somewhere, and nothing is truncated at that level at all. The article vector is
a topical signal instead: title, summary, the full outline, and an even slice of
every section's opening, budgeted so the clamp never silently fires. Round-robin
rather than head-and-tail, because truncating the head of a twelve-section
article stops in the pathophysiology and drops treatment and management — which
is where the words somebody actually searches for live.
`article_section_index` is populated and stays populated. The rebuild was a
private helper in one router, so the three other writers that save sections —
the generation task, the pipeline script and the seeds — silently skipped it.
That is how 323 articles came to have no rows at all. The generator itself is
one line poorer for it now.
A retrieval bug found on the way: the section-to-article rollup concatenated
rather than fused, so a section matching at rank 1 landed behind every weak
whole-article match and never reached the page. And `/articles/?q=` had no
rollup at all.
3,833 vectors in 332 seconds, batched 32 to a request — a normal article save
is now one round trip rather than fourteen. Proved against the vectors restored
from backup: "surgery for infant stridor that fails to improve" found
Laryngomalacia at rank 159, below the floor and invisible; it is rank 1 now, and
the section corpus answers it at rank 1 having previously been unable to answer
it at all.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TqXevQJhxFrM7jJg82cgZN
**Prepared sessions.** Most of this existed: unanswered first, weakest topic
next, wrong-before-right after that, all scaled by what share of the real paper
each topic carries. What it could not do was change with time, say anything
about itself, or be reached without filling in a form.
Evidence now decays on a thirty-day half-life. Exponential rather than a fixed
window because memory has a slope, not a cliff — under a window, 29 days counts
fully and 31 counts for nothing — and because it is memoryless, so an answer's
weight does not shift when unrelated questions are answered, which is what lets
the preview stay a valid forecast. Spring is worth an eighth of last week. Two
things decay: a question's recall probability, drifting towards even rather
than past it, so an old right answer becomes eligible rather than wrong; and a
topic's accuracy, against a prior of two "no idea" answers, which fixes "right
once, known forever".
Strict unanswered-first meant that on a bank of 2,900 nothing was ever
recycled — spaced repetition existed and was unreachable. Review now takes up
to two fifths of a session. And the damping that spread the picks across topics
was applied only to seen material, so a learner with no history was handed the
heaviest domain entire instead of a spread; that was live.
The plan is the product. It is computed, shown, and then the session is built
from that plan's own ids and the plan returned with it, so the two cannot
differ; every figure in it is a tally over the chosen questions rather than a
forecast. No model touches the ranking — a learner asking "why these twenty"
has to get the same answer twice.
**Vision.** The proxy's own `/model/info` says which models can see, so nothing
is hard-coded: 77 report yes, 11 no, and 328 say nothing at all, which means
absent rather than incapable — so those are asked once with an 8px PNG and the
refusal cached. The deployment's main model turns out not to see, and questions
carry figures the learner is looking at, so the tutor was answering about an
image it had never been shown. It routes to a configured tool model now, folds
the description back in as text saying plainly where it came from, and caches
on the bytes because the same figure is re-sent every turn.
Also fixed on the way: `article` was missing from the admin's task list, so
article drafting always ran on the fallback model whatever an administrator
chose; and `.jpx` stem images were sent as JPEG because `mimetypes` guesses
that from the name, so the provider rejected them two hops later.
An administrator must pick a tool model in Settings → AI models. Until then the
tutor says a figure exists that nothing could read, rather than describing one
it cannot see.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TqXevQJhxFrM7jJg82cgZN
Daniel