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Three pairs of docs described the same thing twice, and the copies had drifted
apart. Merged each into one file, keeping the unique content from both:
- ARCHITECTURE.md -> architecture.md (its operational map: ownership, request
flow, runtime boundaries, source of truth, deployment shape)
- DEVELOPMENT.md -> developer-guide.md (change workflow, Clinical Assistant
high-risk areas, frontend rendering rules, deployment checks)
- transcription-options.md -> speech.md (the clinic setup table, and the list
of browser-Whisper paths that must stay removed)
Then audited what remained against the code and the live database rather than
against the previous docs. Corrected:
- Google Vertex was still documented as a provider across nine files. The SDK
is gone; AI_PROVIDER=vertex now logs an advisory and falls back to
OpenRouter, and Gemini is reached through LiteLLM. Fixed the provider
selection order to match src/utils/ai.js, which starts from LITELLM_API_BASE.
- promptSafe was documented on 8 routes; it is on 13.
- Node 20 -> 24, "24 vanilla JS modules" -> no fixed count, and
transcribe.js/tts.js -> sttProvider.js/ttsProvider.js, which is what exists.
- STT/TTS are LiteLLM-only; README listed direct Google, AWS Transcribe and
ElevenLabs paths that are not in the runtime.
- Learning Hub PPTX export was documented as pptxgenjs, which is not a
dependency. It is pandoc against a reference deck.
- POST /api/admin/milestones/seed does not exist; it is /bulk-import.
- NEXTCLOUD_URL and NTFY_TOPIC are not read anywhere. Nextcloud is per-user in
the users table, and the ntfy topic is derived as pedscribe-{userId}.
- A prose paragraph sat inside the Clinical Assistant settings table, so half
the rows rendered as text.
Filled the gaps the audit exposed:
- database.md was missing 12 of 29 tables, including user_resources,
personal_notes, login_codes, registration_invites and generated_image_jobs.
- developer-guide.md was missing 11 routers and 10 frontend modules.
- api-reference.md detailed 121 of 244 endpoints and said so, but whole
features were absent. Added an endpoint index covering Clinical Assistant,
My Resources, Notes, Diagrams, ED Encounters, invites and sign-in codes.
- configuration.md was missing METRICS_TOKEN, REDIS_URL, API_RATE_LIMIT_MAX,
the LITELLM_* model variables, the DB_* ones maintenance.js reads, and the
per-purpose S3 resolution scheme.
- clinical-assistant.md documented 2 of its 17 environment variables.
- features-explained.md had no entry for My Resources or Clinical Assistant.
Renamed the three remaining SHOUTING filenames to kebab-case, which is what the
docs viewer's prettyName() was working around, and rewrote README's index,
which listed architecture.md twice and omitted nine files.
Noted but not changed: the Turnstile site key is hardcoded in index.html rather
than read from TURNSTILE_SITE_KEY, and /api/health/detailed can report
tts: 'elevenlabs' though no ElevenLabs path exists.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Dv6sqaY6Vq3ChZHMem3cnU
101 lines
3.9 KiB
Markdown
101 lines
3.9 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 / TXT / MD / HTML / CSV / JSON, ≤ 100 MB each, max 10 files |
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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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