5 commits

Author	SHA1	Message	Date
Daniel	025290d64a	feat: retire Learning Hub ... My Resources generates better slides than Learning Hub ever did — a typed deck the model fills in, rendered by python-pptx with fit-to-slide text, figures, a vision review and themes, against Learning Hub's markdown-through-pandoc — and the articles and quizzes now live in the quiz app. Keeping a second, weaker generator and a whole CMS beside it was not earning its maintenance. Removed: three routers, the Learning Hub and Content Manager tabs, their components and frontend modules, the five database tables, the WebDAV browser, the content embedding column and its vector index. Content was exported first — every article as markdown plus a full SQL dump of all five tables — to ops-backups/learning-hub-export-*. That export is the restore path; the migration's down() can recreate the shape but never the rows, and says so. Two things this simplifies rather than merely deletes: generated_image_links existed only to record which published content an image appeared in, and it was the sole reason a generated image could be read by someone who did not make it. Images are now owner-only — the visibility rule is one WHERE clause instead of a join across two tables and a published flag. embeddings.js keeps the model discovery the admin panel uses and loses searchSimilar and generateContentEmbedding, which queried a table that no longer exists. Kept deliberately: Nextcloud connect, disconnect and export, which are how a generated note reaches a real filesystem and have nothing to do with Learning Hub; learningRetrieval, which despite its name is the clinical corpus search My Resources depends on; and the pandoc reference deck, still the fallback when the python renderer fails, moved from assets/learning to assets/deck now that the old name misleads. Tests: four Learning-Hub-only files removed, and the individual cases inside shared files that asserted its behaviour. Where a test used a Learning endpoint only as a convenient example — the account-boundary token test, the policy matrix — it now uses one that still exists, so the property it proves is unchanged. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Dv6sqaY6Vq3ChZHMem3cnU	2026-09-12 20:14:20 +02:00
Daniel	99a72734fb	feat: the Learning screen can ask for grounding, and says what it got ... The route accepted useCorpus and reported grounding, but nothing in the admin screen sent the flag or showed the result — so the feature existed and was unreachable. Opt-out in the UI rather than opt-in. For clinical teaching the library is nearly always the right source, so someone who never notices the checkbox should get the grounded version. The help text explains when to turn it OFF, which is the non-obvious case: a topic the library does not cover is better written without grounding than padded with the nearest unrelated excerpts. Afterwards it says what happened — "Written from 12 library excerpts", or "Not grounded — nothing indexed matched. Written from the model alone." Ungrounded material presented as grounded is the failure worth preventing here, so the wording never implies the library was used when it was not. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Dv6sqaY6Vq3ChZHMem3cnU	2026-09-11 14:39:49 +02:00
Daniel	94f1290aae	fix: references at the end, never in the body ... A slide carrying [1] markers is unreadable from the back of a room, and an article that cites inline reads as a paper rather than as teaching material. The model is now told explicitly not to cite in the body — no bracketed numbers, no parenthetical "(Nelson, p. 2604)" inside sentences — and to put everything it drew on in a References section at the end, which in a presentation is the final slide. Checked rather than assumed: a six-slide deck generated through the grounded path contains zero in-text citation markers, and ends with a References slide. The prose keeps the specificity that grounding is for — bilirubin produced at two to three times the adult rate, conjugation immature until about two weeks, thresholds in mg/dL — without a single marker interrupting it. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Dv6sqaY6Vq3ChZHMem3cnU	2026-09-11 14:37:47 +02:00
Daniel	f7cd8b39a3	feat: a grounded resource ends with the references it drew on ... The grounding metadata went to the API response and the logs, which is no use to someone holding the deck. A teaching resource shown to trainees should carry its own provenance, so a grounded one now ends with a References section — the final slide in a presentation — listing the library excerpts it actually used, by title and page. Restricted deliberately: only excerpts actually drawn on, nothing invented. That was worth checking rather than trusting. Generated a deck and compared every citation against the source metadata: "Kliegman R. Nelson Textbook of Pediatrics, 22nd ed., 2024, p. 2604" against a stored title of "Kliegman R. Nelson Textbook of Pediatrics 2-Volume Set 22ed 2024" at page 2604, and the same for Fleisher & Ludwig, Rosen's, Understanding Pathophysiology and the AAP compendium. The model reformatted filename-derived titles into readable citations using only what it was given. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Dv6sqaY6Vq3ChZHMem3cnU	2026-09-11 14:23:49 +02:00
Daniel	4f5687982d	feat: Learning resources can be grounded in the clinical corpus ... Learning generated everything from the model alone. A deck on bronchiolitis was whatever the model remembered about bronchiolitis, with no connection to the documents this institution actually indexed — while the assistant had been searching that corpus all along. Same collection, deliberately. mcp_bge_m3_1024 is already embedded with openrouter-bge-m3 at 1024 dimensions; a second index over the same documents with the same embedder would be a copy that drifts. What differs is the budget: a chat answer wants a few tight excerpts because the reader is waiting, a teaching resource synthesises a whole topic. So learning.search_limit and learning.context_chars default to 30 and 2500 against the assistant's 8 and 1400, and are separate keys so tuning one cannot move the other. Not unbounded, though. "No limit" only moves the ceiling from a setting to the model's context window, where overflow truncates the middle of the prompt silently — the worst place to lose source material. 60 results and 8000 characters per excerpt are the caps. Opt in per generation: a resource on something the library does not cover is better written without it than padded with the nearest unrelated excerpts. Retrieval never fails a generation — the resource is then written from the model alone, which is what happened before this existed — and every response reports what it was grounded on, so a caller can say "24 excerpts" or "the library had nothing on this" rather than quietly serving ungrounded material. Verified against the live corpus: bronchiolitis, neonatal jaundice and febrile seizure each returned 12 excerpts and ~23k characters from Nelson, Rudolph and the Pediatric Clinical Practice Guidelines. A deck generated through the full chain came back with textbook specificity that is not general recall — bronchiolar diameter, birth-weight thresholds, the full pathogen list. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Dv6sqaY6Vq3ChZHMem3cnU	2026-09-11 14:03:05 +02:00