Retrieval could not say "nothing". `hybrid_ids` fuses two rankers by reciprocal
rank and throws the distances away, and it returns the union — so the shortlist
was never empty, the "nothing matches" branch never fired, and a question about
photosynthesis came back with six paediatric sources and an instruction to
answer only from them.
So the fix is not more scenarios in the prompt. It is one calibrated number,
and three short prompts chosen by it in code. Asking a model to work out which
situation it is in is the part that does not work, and it is also the part that
makes prompts long.
Measured against this corpus with the bodies now embedded — eight clearly
on-topic questions and eight clearly off-topic:
off-topic 0.339 – 0.499 the French revolution … photosynthesis
on-topic 0.586 – 0.740 what causes croup … posterior urethral valves
The thresholds sit in the gap. They are deliberately not the retrieval floor:
that one decides what is worth putting in a list, where a weak hit costs a
reader a glance. These decide whether an answer claims to come from the
library, and a wrong claim costs them their trust in every other answer.
Above 0.55 the answer is sourced and cited, as before. Between 0.50 and 0.55 it
says nothing covers this directly, names what the closest material is, and
marks which parts came from where. Below, it says so in one line and then helps
anyway from general knowledge, citing nothing — refusing outright reads as a
broken assistant rather than a careful one, and the shortlist is not handed to
a model that has just been told the library does not cover the question.
An unmeasurable closeness is not a low one. No vector database or a downed
encoder returns None, and retrieval still found its rows by other means, so
those are still cited; dropping every citation because the ruler is missing
would be the worse failure.
Also: only published articles are indexed now. A draft is unfinished by
definition and has no business in a search result or in that shortlist. The
index follows publication both ways, and the fifteen-minute sweeper drops rows
whose article has been deleted or unpublished — an article that is never edited
again would otherwise keep its rows for good.
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
**Linking.** A question could be tied to an article only from the article, by
typing the question's number into a box — so opening a question you had just
linked showed no sign of the link, and there was no control to add one. Both
ends now search: find the article by title from the question, find the question
by stem from the article, pick which section of the article the link lands on,
and see what is already linked. One shared finder, so the two ends of one
relationship cannot describe it differently. `GET /questions/{id}/articles`
mirrors the endpoint that already existed the other way, and `GET
/articles/linked` is retired — it answered this question by shipping the whole
prose of every linked article to the quiz player for a list of titles.
"Practise this topic" is a reader's control and no longer appears on an editing
screen.
**The player.** The rail was a bordered card floating in the page with a
scrollbar of its own, so a session had two scrollbars side by side and a
collapse handle tucked inside the card's padding. It is a column now: flush,
full height, its own background rather than its own border, the handle on the
boundary it moves, and a progress bar under the count. The bar at the foot is
the bottom edge of the window — three flush segments, no gaps, no pills —
because Exit as a small grey pill beside a large blue Next made leaving look
like the accident.
Study mode no longer asks whether you are sure. Leaving suspends: every answer
is saved, nothing is graded, and it is waiting where you left it — so the
dialog asked permission for something reversible, under a name for something
that does not happen. An exam still asks once, because a block has a clock, and
it now says what it is: "Leave this block?", not "End Session".
Options are lettered. The explanations already are — a stem extracted from a
board PDF says "Preferred Response: E" — so numbering them 1 to 5 left the
reader translating between two labellings of the same five lines. The tutor is
told the same letters, and the answer key is marked against its own option and
declared authoritative, so a model that would have answered differently cannot
tell a student the marked answer is wrong.
"Preferred response" and "Source page 518" are gone: the first labelled a block
that is obviously the answer, the second named a page of a book the learner
does not have. The clocks moved out of a grey strip across the explanation,
where they read as part of the answer, to the foot of the rail with everything
else about the session.
**AI Mode.** Sources are headed and counted at the end, where evidence belongs,
with the practise button after them rather than above. That button appears only
when there is something to build from and says what it will build — it used to
sit under "how can I help you today?" offering to make a session out of
nothing. A cited question opens in place: `/questions/:id` is the editor, so
following one dropped a learner into a form for changing the question they had
just been told about. And a session built from a chat is named like every other
session, rather than after the chat — asking "hi" produced "hi — practice".
Also: two test questions with raw `<p> </p>` in their stems were live in
the bank; retired. And 36 article summaries were written as a table of contents
with the colons filed off — "Peanut allergy prevention and management: LEAP
guidelines by risk tier, risk stratification, and anaphylaxis treatment" — every
noun phrase sounding informative and none of them saying anything. Rewritten as
claims, with the rule added to the prompt that produced them.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TqXevQJhxFrM7jJg82cgZN
claims to hold them
The scaffolding is down. 203 subject, 2,275 disease and 4,281 keyword
tags, and 25,356 links, deleted — backed up first to a 1.9MB JSON of
replayable rows, because "we can always put it back" should be true
rather than said. The 16 system rows stay: categories point at them.
With them go the things that only existed to feed them — the
classify_questions task, its snapshot helpers, POST /tags/classify and
its status poll — and the three Taxonomy tabs that would now always read
zero. A tab showing 0 forever teaches people the page is broken.
The organ-system filter in the session builder moved onto categories with
the rest, including everything beneath a matched topic, so it groups the
way the analysis does.
Registration: `settings:registration_enabled` was set to false, and there
was no switch anywhere on the site to set it back. The API had always
accepted it; the Site policy page had never shown it. So the site could
be closed to new members with the admin looking at three switches, all
correct, and no way to see the one that was actually refusing them. It is
now the first switch on that page, and says plainly that the ones below
it have nothing to act on while it is off. The SSO-only flag was hidden
the same way and is shown when SSO is configured.
Deleting a topic no longer silently unfiles its questions. It asks where
they go, and says how many are waiting, unless the topic is empty — the
same rule promotion now follows. Its extra category links move too,
minus any that would duplicate a pair the destination already has.
Back links: Trash, Extraction jobs, Taxonomy and the Handbook had none at
all, and Access pointed at the wrong section. They are one component now,
each returning one step to the section it was opened from. Editorial has
its own entry in the section bar, so its Tools card is gone rather than
being a second door to the same room.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TqXevQJhxFrM7jJg82cgZN
The phone had a dot grid dropped under the top bar — a different thing
in a different place doing the rail's job worse. It is a drawer holding
the same rail the desktop has, with the site's own menu on the other
tab, because the alternative is a second hamburger elsewhere for the
same purpose. The dot grid and its styles are gone.
And the extraction pipeline was run end to end against a three-question
PDF rather than reasoned about. It works: three questions, stems,
options, correct answers and explanations, landing in a draft batch and
not in the bank. But the run found a real bug on the way.
A document's text is read from the search index, not from the file. When
that index is missing — never processed, or lost to a restart — every
page is skipped and the job fails with "the AI could not find questions
with correct answers in this page range". That is the wrong diagnosis,
and it sends people to change the model, the prompt and the page range,
none of which is the problem. The two failures are now counted apart and
named apart: no stored text says so and says to re-process; a model that
found nothing says that instead.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TqXevQJhxFrM7jJg82cgZN
Extraction wrote straight into `questions`, so a machine's first attempt
took a permanent id the moment it was produced. Ids come from a sequence
and are never reissued: every rejected draft burned one, and every draft
that needed fixing was sitting in the bank while it was being fixed.
A run now lands in a batch of drafts with their own table and their own
sequence. They are read, corrected and decided there, and `accept` is
the only place a Question is created — a copy rather than a translation,
because every field a draft holds is a field a question has, so nothing
is lost at the moment of acceptance.
Accepting is all or nothing, and everything is checked before anything
is created: a call that reports failure must not leave questions behind
from the drafts it got through first. My own test caught that — the
first question existed before the second draft was refused.
Readiness is reported for every draft rather than only on the attempt to
accept it, so a reviewer sees what needs work before opening anything.
A decided draft keeps its row and records what it became, so a batch
reads as a history of what was decided rather than emptying as it is
worked through. An acceptance cannot be undone from here: the question
exists, and deciding twice would make a second one.
No embeddings for drafts. A vector is for finding a question in the
bank, and a draft is not in the bank.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TqXevQJhxFrM7jJg82cgZN
"PREP" is the American Academy of Pediatrics' trademark for their own product.
The plans here are our own sets of questions grouped by year, so they are now
named for what they are: Board Review 2021, and Mixed Review for the plan that
draws from every year at once.
Renamed in the database as well as the code — 13 plans, 14 quizzes a learner had
already generated from a block, and the 12 year tags, which appear in the
question bank's filters and are as visible as the plans. The seeder matches both
the old and new names so a fresh import still finds its material, and the tagger
mints the new one so the next run cannot undo this. Prompts and comments that
described the source PDFs by that name now describe them by what they are.
The generation run's 377 failures were not a bug
Every call was reserving the model's full 64k output ceiling, and OpenRouter
refuses the whole request when the balance is below the reservation — "you
requested up to 64000 tokens, but can only afford 52017" — however short the
answer would actually be. `_call_model` now takes a max_tokens, and the article
writer asks for 4000, which is comfortable for three views of one topic and
keeps each request small enough to be affordable. 98 articles were written
before the balance ran down; 158 exist in total.
Generation is paused at the user's request while credits are topped up.
208 backend, 243 frontend green.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TqXevQJhxFrM7jJg82cgZN
Retrieval generalised beyond questions
`_text_for_question`, `embed_question` and `hybrid_question_ids` all hardcoded
the questions table, so there was nothing to call for an article or a card. That
layer is now corpus-agnostic:
- `Embeddable` mixin gives articles and flashcards the same embedding,
embedding_model and embedded_at columns questions have, plus a weighted
full-text vector (migration u3a4b5c6d7e8).
- `embed_record(row, kind)` is one code path for all three — they share an
embedding space, so they must share the model and provenance rules too.
- `hybrid_ids(db, query, kind)` ranks any corpus; `hybrid_question_ids` stays as
a thin alias for existing callers.
- Article and flashcard search moved off `ILIKE '%term%'`, which could not find
a jaundice article from "yellow newborn".
- The retry task and full regeneration now sweep every corpus, and the health
report breaks down current/stale/missing per kind.
- Articles embed on create and on edit, with failures left to the retry task.
Quoted phrases replace the keyword-only mode
`websearch_to_tsquery` already gives "absence seizure" exact-phrase semantics,
and the semantic ranker sits out a quoted query. That covers the one case a
keyword-only toggle was for — exact lookup — per query rather than as a sticky
setting whose every position returns a subset of the default.
Full-page question editor (/questions/new, /questions/:id)
Editing happened in a cramped modal. There is now a page with room for the stem,
per-option explanations, a searchable category picker with primary plus extras,
difficulty, and images. It shows the question's id with a copy button, and
Duplicate creates a variant without retyping the stem. `GET /questions/detail/{id}`
backs it, pathed under /detail/ so it cannot shadow the static routes.
Question bank filter bar restyled — the toggle and count read as one control
instead of two grey pills crowding the result count.
Tests: 101 backend green.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01PpfzbZ1QTLMeVYxM2kyq8m
Search
- Retrieval was hybrid in name only: the keyword filter was applied to the SQL
query, so results were the *intersection* of the two rankers. A question that
matched the meaning but not the literal string could never be returned. It is
now a union, fused with Reciprocal Rank Fusion (a text rank and a cosine
distance are not on comparable scales, so RRF uses only their orderings).
- Added a generated `search_vector` tsvector + GIN index, so the lexical half is
ranked full text rather than ILIKE substring matching.
- Chose Postgres + pgvector over OpenSearch/Elasticsearch: a search cluster
would add a second datastore to keep in sync and a JVM on this host, to
replace an index Postgres maintains inside the same transaction.
- Removed the keyword-only mode. It looks precise but silently drops the
question that asks the same thing in different words.
Embeddings — measured on 500 real questions, using each question's own
explanation as a paraphrase query (known answer, no hand labelling):
bge-small (local CPU, 384d) R@1 0.840 R@5 0.953 186ms/query
bge-m3 (LiteLLM proxy, 1024d) R@1 0.847 R@5 0.973 93ms/query
BGE-M3 wins on both quality and latency and needs no extra credential, since
llm.danvics.com already serves `openrouter-bge-m3`.
Three gaps this exposed, all fixed:
- Nothing recorded which model produced a stored vector, so changing models
silently mixed incomparable spaces. `embedding_model` / `embedded_at` now
stamp every vector, `GET /admin/embedding/health` reports current vs stale vs
missing, and regeneration defaults to stale-only.
- The generator read the model from env while the stamp read a Redis override,
so a vector could be labelled with a model that did not produce it. Both now
resolve through one function, with a regression test.
- Embedding at creation is best effort, and a failure left a question invisible
to semantic search forever. `retry_missing_embeddings` runs every 15 minutes
via Celery beat and backfills missing or stale rows.
- Query embeddings are cached in Redis per model, so typing is not a network
round-trip per keystroke.
`dimensions` is only sent to OpenAI's embedding-3 family; BGE-M3 rejects it.
Tests: 8 new backend tests (union not intersection, fusion ordering, per-ranker
failure degradation, provenance stamping, stale/missing accounting, generator
and stamp agreement). Full suites green: 95 backend, 127 frontend, build clean.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_014yhHB8Pc7oQqyqn2Vo9DXA
AI refine now sends the current body and sections to the model; invalid model section IDs are replaced with valid hex IDs. Job polling list raised to 200. 50 backend tests pass.
Quiz share links replace the PIN copy with a public /share/{token} landing page; owners can enable/revoke without showing the full link. Moderated article/question comments with approval flow, bounds and rate limits. Educator AI article drafts/refine and private card generation with Celery job polling. Migrations f2a1c9d4e801 and g4b7e2f5a903. 50 backend and 85 frontend tests pass.
Recovery snapshot of the existing worktree before the Orthobullets-inspired revamp. Includes explanation images, classification snapshots, quiz visibility/resume fixes, quiz codes, TTS options and bot formatting. Secret heuristic and Python syntax checks passed; not a release or full behavioral validation.
ai_decide now samples 4 points across the section (start, 1/3, 2/3, end)
instead of just the first 30 + last 20 pages. This gives accurate strategy
detection on large documents where the answer format might be deeper in.
New ai_answer extraction mode:
- Extracts questions from Q&A-format PDFs that have no answer key
- AI picks the correct option from each question's choices
- Generates explanation using document context + medical knowledge
- Useful for PDFs like practice tests where answers were never included
- Available manually and as an ai_decide strategy
Flashcard decks can now be renamed:
- PATCH /flashcards/{deck_id} updates title
- Inline edit on FlashcardsPage with responsive layout (input full-width,
buttons wrap under it so Cancel never overflows the card)
- Title truncates with ellipsis when not editing
Note: generate mode (textbook -> MCQs) is unchanged per user request.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Fix: flashcard task used wrong Redis key prefix (job: vs extraction:)
causing progress to stay on "pending" after completion
- Flashcards are now user-scoped: each user sees only their own decks
- Soft-delete decks: DELETE moves to trash, ?permanent=true to destroy
- Trash tab on flashcards page: restore or permanently delete decks
- Delete individual cards with inline confirmation in browse view
- Card edit/delete now allowed for deck owner (not just moderators)
- ExtractionProgress label prop: shows "Generating Flashcards" not
"Extracting Questions" for flashcard jobs
- Added deleted_at column to flashcard_decks
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
New feature: generate flashcards from PDF sections using AI, completely
separate from the existing quiz system.
Backend:
- FlashcardDeck + Flashcard models with cascade deletes
- flashcard_tag_links table for tag classification (reuses question_tags)
- /api/flashcards/ router: CRUD for decks, browse/search cards, tag filtering
- generate_flashcard_deck Celery task with chunked processing + progress
- FLASHCARD_PROMPT in extraction_modes.py (15 cards per chunk)
- "flashcard" added to admin model task types
Frontend:
- FlashcardsPage: deck grid + card browser with search/filter
- FlashcardStudyPage: flip cards, mark known/review, keyboard nav,
shuffle, progress bar, completion screen
- DocumentDetailPage: "Create Flashcards" button alongside "Extract Quiz"
- Navbar: Flashcards link
- AdminPage: flashcard in model task dropdown
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- teach.py: use _proxy_model() + pass api_key/api_base from settings (fixes LiteLLM provider error for openrouter/bedrock models)
- teach.py: accept model_id in ChatRequest so frontend can select model
- main.py: remove titan-embed-v2 from general seed, auto-delete legacy entry on startup
- main.py: kill stale idle-in-transaction DB connections at startup to prevent DDL lock hangs
- main.py: set lock_timeout=10s on DDL connection as fast-fail safety net
- TeachChat.jsx: fetch /teach/models, show selector dropdown in header when >1 model available
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Embedding:
- Embedding model now configurable via Admin UI (More tab) or LITELLM_EMBEDDING_MODEL env
- Calls LiteLLM proxy directly via httpx (bypasses LiteLLM library param validation)
- Passes dimensions=1024 to proxy; Redis setting overrides env var
- Default model: ge-gemini-embedding-001 (Gemini AI Studio, 1024-dim)
- Test button in admin UI to verify model works
- Fixed vector_service to use httpx + Redis model (was broken with non-prefixed model names)
Polly:
- Global enable/disable toggle in Admin → More settings (stored in Redis)
- /tts/voices filters out polly/* when disabled
- /tts/speak rejects polly requests when disabled
Job cancellation:
- POST /quizzes/job/{job_id}/cancel endpoint
- Cancel button on JobsPage for running jobs
- Celery task checks Redis status at each chunk boundary and exits cleanly
- Fixes DB lock on restart caused by cancelled jobs leaving open transactions
Admin UI:
- Settings tab renamed to "More" (heading: More Settings)
- Model row overflow fixed (minWidth: 0 + ellipsis on model_id)
- Embedding model search shows all proxy models (no auto-filter by "embed")
- Navbar correctly excludes cancelled/failed jobs from "extracting" count
README:
- Added Rebuild & Restart section with commands
- Updated embedding model reference
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
The job ID was pushed to extraction:user_jobs:{uid} twice:
1. quizzes.py router when dispatching the job
2. quiz_tasks.py Celery task when it starts running
Removed the push from the Celery task. Router handles it.
Also cleaned 13 duplicate entries from Redis.
Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com>
Extraction modes (no restart needed — code ready for next Celery deploy):
- New QuizCreate.extraction_mode field: standard|questions_only|two_step|regex|ai_decide
- extraction_modes.py: independent implementations that don't touch standard path
- questions_only: extract Q+options, correct_answer="PENDING" for manual fill
- two_step: separate answer key section scan + phase1/2/3 matching
- regex: AI detects answer pattern, generates regex, applies to full doc
- ai_decide: AI reads samples from start+end and picks strategy
- DocumentDetailPage: Extraction Mode dropdown with description per mode
- quiz_tasks.py: routes to correct mode, standard path completely unchanged
Database:
- Deleted 11 orphaned questions from PREP 2013 extraction (quiz 12 was already deleted)
- 268 questions remaining (all PREP 2012)
UI fixes:
- Nextcloud section in Settings now only shown to moderators/admins
(regular users can't upload PDFs so they don't need Nextcloud)
- Upload PDF already hidden in navbar for non-moderators (confirmed correct)
- Resume quiz: now async — study mode quiz data loaded BEFORE showing quiz
so correct_answer is available immediately for feedback
- Resume saves and restores voice selection
- voice field added to ProgressSave schema and Redis storage
- Progress save dependency includes selectedVoice
Attempts:
- POST /attempts/start: reuses existing incomplete attempt by default (fresh=false)
Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com>
- Completely reverted quiz_tasks.py to simple standard extraction with chunking
(no more two-phase detection that broke PREP 2012/2014)
- OCR normalization kept: 'Pref erred'→'Preferred', 'ltem'→'Item'
- The extraction prompt already handles both 'Correct Answer: X' and
'Preferred Response: X' inline formats — no special detection needed
- PREP 2013 (separate answer key) will be implemented as a separate option
user selects at extraction time, not automatic detection
Also in this commit:
- Fixed quiz delete 500 error (source_quiz_id attribute name)
- Added trash bin (soft delete, restore, permanent delete)
- Added hide/publish toggle per quiz (moderators see all, users see published only)
- Quiz progress saved to Redis — survives logout, works cross-browser
- Resume in-progress quiz from any browser
- ConfirmButton component replaces all window.confirm/prompt
- Delete own attempts endpoint
- TrashPage, AdminPage trash/jobs links in Settings
Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com>
Critical bug fix:
- DELETE /quizzes/{id} was returning 500 due to {\"quiz_id\": None} update using
the DB column name instead of Python attribute name (source_quiz_id).
Fixed with {QuestionModel.source_quiz_id: None, synchronize_session=False}
Extraction fix:
- Two-phase extraction was incorrectly triggering for PREP 2012/2014 format
documents that have 'Preferred Response:' in their explanation text.
Fix: check for inline 'Correct Answer:' first — if found, always use standard
extraction regardless of 'Preferred Response:' appearing elsewhere.
Quiz trash bin:
- DELETE /quizzes/{id} now soft-deletes (sets deleted_at)
- GET /quizzes/trash — list deleted quizzes (moderator)
- PATCH /quizzes/{id}/restore — restore from trash
- DELETE /quizzes/{id}/permanent — permanent delete (must be in trash first)
- TrashPage.jsx — accessible via Settings → Admin → Trash
Hide/publish quizzes:
- is_published column on quizzes (1=visible, 0=hidden)
- PATCH /quizzes/{id}/publish?published=false — hide from regular users
- Moderators see all quizzes; regular users only see published
- 👁/🙈 toggle button per quiz card (moderators only)
Quiz progress resume (cross-browser via Redis):
- POST /attempts/progress — save {answers, current_idx, mode} to Redis (7 days)
- GET /attempts/progress?quiz_id=N — retrieve saved progress
- DELETE /attempts/progress/{quiz_id} — clear on submit
- QuizPage auto-saves to Redis every 1.5s (debounced) while in progress
- ModeSelectScreen loads saved progress from server, shows Resume button
- Works across browsers, devices, and after logout
Delete attempt:
- DELETE /attempts/{id} — user can delete own attempt + clears reminders for that quiz
ConfirmButton component:
- Replaces all window.confirm() / window.prompt() across the app
- Double-click pattern: first click shows [Confirm] [Cancel] inline
- Applied to: QuizzesPage, DocumentDetailPage, QuizEditPage, QuestionBankPage
Category delete (QuestionBankPage):
- window.prompt() replaced with inline modal dialog with select dropdown
- User chooses where to move questions before deletion
Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com>
The 'Chunk X/5' message was showing 5 even when only 2 chunks were
being processed (pages 1-50 and 51-55). n_chunks was not updated
after filtering chunks to stay before answer_section_start.
Cosmetic fix — the actual extraction was already correct.
Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com>
Extraction fixes:
- OCR normalization: 'Pref erred' → 'Preferred', 'ltem' → 'Item' applied to boundary
scan, Phase 1 questions, and Phase 2 answer key content before AI processing
- Chunk boundary: Phase 1 chunks now capped at (answer_section_start - 1) so no
chunk bleeds into the answer section — (51, 100) becomes (51, 55) for PREP 2013
- Result: Phase 1 gets 2 clean chunks (1-50 and 51-55), Phase 2 gets pages 56-227
Category creation in DocumentDetailPage:
- Replaced window.prompt() with inline input form (more reliable, no browser quirks)
- Fixed option value type: String(c.id) ensures consistent string comparison with
selectedQuestionCategoryId state (prevents type mismatch in controlled select)
- "+ New" button toggles inline form; Enter key or Add button submits
Deletion safety (confirmed):
- Deleting a quiz: questions detached to bank if exclusive, kept if shared — NEVER deleted
- Deleting a question category: questions uncategorized or moved — NEVER deleted
Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com>
Two-phase extraction improvements:
- Auto-detect answer section boundary by scanning in 10-page steps for
'Preferred Response:' — finds exact page where questions end and answers begin
(PREP 2013 answers start at page ~68, not at the end of the file)
- Restrict Phase 1 question chunks to pages BEFORE the answer section
- Extract answer key from answer section in CHUNKS (50 pages each) to handle
large answer sections — accumulates all item→letter mappings
- Previous version used last 40% which missed items 1-~135 for PREP 2013
README: full CLI extraction documentation:
- list-sections: find document and section IDs
- extract <section_id> [--bg] [--title] [--mode] [--user]
- jobs / jobs --user <email>
- Explanation of auto-format detection (inline vs separate answer key)
Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com>
Two-phase extraction:
- Detects end-of-document answer key format by scanning last 40 pages for
"Preferred Response:" (PREP 2013, 2014 etc use this vs PREP 2012 inline "Correct Answer:")
- Phase 1: Extract questions with item_number field, allow null correct_answer
- Phase 2: Extract answer key (item_number → letter) from last 40% of document
- Phase 3: Match questions to answers by item number, resolve letter → full option text
- Unmatched questions go to skipped list with reason shown in Jobs page
- Standard inline format (PREP 2012) unchanged
Updated extraction prompts:
- item_number field added to all extractions for cross-referencing
- Image content rule: "Item CXXXB" figure references must NOT be treated as new questions
- Recognises both "Correct Answer: X" and "Preferred Response: X"
- ANSWER_KEY_PROMPT: dedicated prompt for extracting answer key tables
Quiz navigation scroll:
- Clicking Next, Previous, or question number now scrolls the question card
into view (smooth scroll to start of question-card div)
Code: extract_questions_no_answers(), extract_answer_key(), _call_model() added to ai_service.py
Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com>
Jobs (cross-browser/cross-session):
- POST /quizzes/ stores job_id in Redis under user key (extraction:user_jobs:{uid})
- GET /quizzes/jobs returns all recent jobs for current user from any browser/session
- Navbar JobsBadge polls /quizzes/jobs API every 4s (not localStorage)
- Shows all recent jobs with status badges; links to quiz when complete
- Badge visible even after extraction completes so you can always get back
Mobile navbar fix:
- .navbar .container height was overriding dropdown to 52px (clipping all links)
- Fixed by using .navbar-inner class for the header row only
CLI extract command:
python manage.py list-sections [doc_id] — list docs + sections with IDs
python manage.py extract <section_id> — inline blocking extraction
python manage.py extract <section_id> --bg — background via Celery
python manage.py jobs — show all extraction jobs in Redis
python manage.py jobs --user <email> — filter by user
Quiz delete + question bank:
- When a quiz is deleted, questions that belong ONLY to that quiz are deleted
- Questions shared with other quizzes (via junction) are kept in the bank
Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com>
Extraction is now fully async via Celery — UI shows a live progress panel,
job continues even if page is closed. Large documents are processed in
50-page chunks to extract all questions (not just first ~50 pages).
Backend:
- app/tasks/quiz_tasks.py: new Celery task 'extract_quiz'
- Writes step-by-step progress to Redis (extraction:steps:{job_id})
- Splits large page ranges into 50-page chunks, processes each separately
- Reports per-chunk results and running total
- Falls back to synchronous if Celery/Redis unavailable
- POST /quizzes/ now returns {job_id, status:"pending"} immediately
- GET /quizzes/job/{job_id} polls progress: steps[], status, quiz_id on completion
- Celery task list updated to include quiz_tasks
Frontend (DocumentDetailPage):
- ExtractionProgress modal component: monospace step log, auto-scrolls, spinner
- Polls job status every 2 seconds via /quizzes/job/{job_id}
- "Open Quiz →" button appears when done
- "✕ closes — job continues in background" shown while running
- beforeunload warning when job is active (preventing accidental close)
Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com>
