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The ceiling was 100 MB per file with ten files allowed at once, and every file is held whole in memory to be parsed — so the old limit let a single request ask for a gigabyte of heap. A source article that size is not a thing anyone uploads here. Now 10 MB, defined once and used by both the multer limit and the post-upload check. The filter accepted `allowed mime OR allowed extension`, so naming a file .pdf was enough on its own, whatever it declared — and the extension is chosen by whoever uploads. Both are required now. Neither of those sees any bytes: multer filters on the headers, before the file has arrived. verifySources() runs once the buffer exists and refuses a file whose contents are not what its type claims, using the same helper as documents, S3 uploads and assistant attachments. It runs before extraction, because an extractor handed a malformed file is where the damage would happen. The CMS screen said 100 MB and listed four of the ten accepted formats; it now says what the server actually does. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Dv6sqaY6Vq3ChZHMem3cnU
101 lines
4 KiB
Markdown
101 lines
4 KiB
Markdown
# Learning Hub
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A CMS + content-delivery module for clinical education material inside the
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app. Supports articles, clinical pearls, quizzes, and Marp-rendered
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presentations with PPTX export. Quiz questions are stored alongside article
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content and can optionally be generated by AI from uploaded source material.
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## Content types
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| Type | Description |
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| `article` | Rich HTML body with an optional attached quiz |
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| `pearl` | Short clinical snippet (no quiz, no heavy media) |
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| `quiz` | Standalone quiz (no article body) |
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| `presentation` | Marp markdown rendered as slides; PPTX export supported |
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## User-facing features
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- Browse by category.
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- Three search modes:
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- **Keyword** — Postgres full-text.
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- **Semantic** — pgvector cosine similarity on the embedding column.
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- **Hybrid** — weighted merge of both result sets.
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- Articles render with sanitized HTML (DOMPurify, loaded via SRI-pinned cdnjs).
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- Quizzes: multiple-choice, multi-select, true/false. Score computed on submit,
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per-question explanations revealed after.
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- Presentation viewer: modal with keyboard / swipe navigation.
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- Progress: `learning_progress` stores per-attempt score + total.
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## CMS (moderator / admin)
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- Tiptap rich-text editor for article body.
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- Draft / published toggle.
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- Category assignment.
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- Quiz builder: add/remove questions, add/remove options, mark correct, enter
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explanation.
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- Marp editor for presentations with live preview.
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## AI content generation
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`POST /api/admin/learning/generate` takes one of:
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| Input | Notes |
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| `topic` | Plain-text description of the topic |
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| Uploaded files | PDF / DOCX / PPTX / ODT / EPUB / TXT / MD / HTML / CSV / JSON, ≤ 10 MB each, max 10 files. The declared type must be in the allowlist *and* match the extension, and the bytes are sniffed before anything parses them. |
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| WebDAV path | Pulled from the user's connected Nextcloud instance |
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Parameters: `model` (from the provider whitelist), `slideCount` for
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presentations, `wordCount` for articles.
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File uploads pass the `src/utils/fileType.js` magic-byte check so a
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mismatched extension is rejected before it reaches the parser.
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## Marp → PPTX export
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`POST /api/admin/learning/generate-pptx` writes the markdown to a temp
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directory and runs `pandoc` against
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`assets/learning/slides-reference.pptx`. The reference deck carries the fonts,
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palette and slide layouts, so restyling the export means editing that file in
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PowerPoint — not changing code.
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Images are handled before pandoc sees the markdown. Each
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`/api/generated-images/{id}` link is resolved through the ownership check and
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written beside the deck under a name this route chooses; any link that does not
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resolve to one of those is dropped. Pandoc resolves image links against the
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filesystem, so passing an arbitrary local path through would embed that file
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into the deck.
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This is the Learning Hub's own path and is separate from My Resources, which
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renders decks with python-pptx from a typed deck rather than from markdown —
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see [`my-resources.md`](my-resources.md).
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## Semantic search
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| Store | `pgvector` on `learning_content.embedding VECTOR(768)` |
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| Index | IVFFLAT, cosine distance |
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| Primary model | `vertex/text-embedding-005` (768 dims), served through LiteLLM |
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| Fallback model | OpenAI `text-embedding-3-small` (truncated to 768 to match the column) |
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Embeddings are generated on content publish + on every edit. If the embedding
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provider is unreachable, the content still saves — keyword search remains
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available.
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## Tables
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| Table | Purpose |
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| `learning_categories` | Top-level groupings |
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| `learning_content` | Articles / pearls / quizzes / presentations. Body + `embedding` vector. |
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| `learning_questions` | Quiz question prompts (FK to content) |
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| `learning_options` | Answer options (FK to question) |
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| `learning_progress` | Per-user attempt history |
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## Retrieval sizing
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How many corpus excerpts the Clinical Assistant, the Learning Hub and My
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Resources each receive, and the reranker cap that overrides all three:
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[retrieval-tuning.md](retrieval-tuning.md).
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