**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
Images were findable only by the filename someone typed. `media_assets` gives
them a title, caption, alt text, a category on the shared tree and tags, with a
weighted tsvector so they are searchable now (migration y7e8f9a0b1c2).
The embedding column is filled from the caption today. A vision-capable model can
fill it from the image itself later without another migration — and because
`embedding_model` stamps every vector, a text-embedded caption and a
vision-embedded image stay distinguishable instead of being silently mixed in one
index. Adding "media" to the embeddable kinds is all the retry task, the full
regeneration and the health report needed.
`media_tag_links.tag_id` carries no ORM-level foreign key: `question_tags` is
created by raw DDL rather than a model, so the constraint lives in the migration
where the table actually exists.
Tests: 113 backend green.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01PpfzbZ1QTLMeVYxM2kyq8m
An article embedded as a single vector, which finds the article but not the
paragraph — so a citation could only ever point at the top of a page. Sections
live in a JSON column and cannot carry a vector or a full-text index, so they are
now projected into `article_section_index`: one row per section with its own
embedding and weighted tsvector (migration x6d7e8f9a0b1).
- Rows are keyed by section id, so editing a section updates it, removing one
deletes it, and an unchanged section is not re-embedded on every save.
- `article_section` joins the embeddable kinds, so the retry task, the full
regeneration and the health report cover it without further changes.
- `hybrid_ids(db, query, "article_section")` searches it like any other corpus.
This is the groundwork for grouped search results (article, then the sections
that matched) and for AI citations that deep-link to the right section.
Tests: 113 backend green.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01PpfzbZ1QTLMeVYxM2kyq8m
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
- Fix double /v1 in TTS audio/speech URL when LITELLM_API_BASE includes /v1
- Fix double /v1 in embedding service and vector service URLs
- Clean up docs: remove second-person language in deployment, frontend, migrations
Co-Authored-By: Claude Opus 4.6 (1M context) <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>
