learning-platform/AI_AGENT.md

# AI Agent Context Guide: Respellion Learning Platform

Welcome, fellow AI agent! If you are reading this, you are tasked with maintaining or extending the Respellion Learning Platform. This document provides the critical context, architectural patterns, and design standards you need to successfully work on this codebase.

> **Last updated:** 2026-05-26 — Per-user curriculum start (employees enroll on first login; week/cycle derived from each user's `curriculum_started_at`, no longer from the ISO calendar week). Documents the real React/Vite + PocketBase stack, TF-IDF retrieval, 3 learning-content types, 3 micro-learning types, and the tiered Claude model setup.

## 1. Architectural Overview
This is a single-page React application built with **Vite**, backed by **PocketBase** as the database and auth layer. There is no separate backend server — the browser talks directly to PocketBase, and to the Anthropic API through a reverse proxy.
*   **Frontend:** React 19, React Router 7, Vanilla CSS (via CSS variables) + Tailwind v4 utilities mapped to those variables.
*   **Backend:** PocketBase (self-hosted, SQLite). All data is stored in PocketBase collections, not localStorage.
*   **Animations:** Framer Motion (page transitions, podium effects, gamification feedback).
*   **Icons:** Lucide React.
*   **Visualizations:** D3.js (used strictly for the Admin Knowledge Graph).
*   **Retrieval:** A dependency-free TF-IDF index over the knowledge graph (`src/lib/retrieval.js`). There is **no Qdrant and no embeddings API** — older specs that mention them describe a design that was never built.

> The top-level `app/` directory is abandoned Next.js scaffolding from that original design. It is not built or deployed. Ignore it; the real app is `src/`.

## 2. State Management & Storage (Critical)
All persistent data lives in **PocketBase**. The data access layer is in `src/lib/db.js`, which wraps the PocketBase SDK client from `src/lib/pb.js`.

**PocketBase Collections (current):**
*   `topics` — Knowledge graph nodes (`id`, `label`, `type`, `description`, `learning_relevance`, `relevance_locked`, `theme`, `complexity_weight`, `difficulty`).
*   `relations` — Knowledge graph edges (`source`, `target`, `type` — `related_to` / `depends_on` / `part_of` / `executed_by`).
*   `content` — AI-generated learning modules per topic (`topic_id`, `data`). The `data` field is a **merged JSON object** containing only the content types generated so far (e.g. `{ article: {...}, slides: [...] }`). New types are shallow-merged in by `learningService.js`; nothing is overwritten.
*   `micro_learnings` — Generated micro-learning artifacts (`topic_id`, `type`, `content`, `status`). One record per topic per type. `status='published'` items are visible to employees.
*   `micro_learning_completions` — Append-only completion events (`team_member_id`, `micro_learning_id`, `topic_id`, `type`, `session_week`).
*   `curriculum_versions` — Versioned 26-week schedules (`version_number`, `status`, `generation_reason`, `confirmed_by`, `confirmed_at`, `schedule` JSON, `coverage_stats` JSON). Exactly one `active` at a time.
*   `leaderboard` — Points ledger (`user_id`, `name`, `points`, `tests_completed`, `learnings_completed`).
*   `team_members` — Registered users with PIN auth (`name`, `role`, `pin`, `curriculum_started_at`, `enrollment_status`).
*   `sources` — Uploaded source documents and extraction status (`name`, `status`, `error`, `progress`).
*   `settings` — Key/value store (`key`, `value`).
*   `llm_calls` — Per-call telemetry (`task`, `model`, `tier`, `duration_ms`, token counts, `stop_reason`, `ok`, `error_msg`).

**Dropped collections:** `quiz_banks`, `quiz_results`, `quiz_cache`, `learn_progress`, and the legacy `curriculum` (v1) collection no longer exist. The matching `db.js` helpers are deprecated stubs — do not build on them.

**localStorage** is only used for **admin browser settings** (not user data):
*   `admin:model:fast` / `admin:model:standard` / `admin:model:reasoning` — per-tier model overrides (legacy `admin:model` still honored for `standard`).
*   `admin:use_simulation` — when true, `callLLM` returns stub data instead of calling Anthropic. Useful for UI work without spending tokens.
*   `kb:suggestions` — Pending/approved/rejected graph deltas proposed by R42. Always mutated via `kbStore` (see §9).
*   `quiz:active:{userId}` — Boolean flag set while a user is mid-quiz. R42's launcher is hidden when this is true (quiz-integrity rule).
*   `chat:thread:{userId}` — Persisted R42 conversation, capped at 50 messages.

**Session:** Login is PIN-based. The logged-in user's ID is stored in `sessionStorage` under `respellion_session` and resolved against `team_members` on app load (`src/store/AppContext.jsx`).

**Per-user curriculum position (important — changed):** Each employee starts the curriculum when *they* choose. On first login a blocking onboarding screen (`/onboarding`) records `curriculum_started_at` and flips `enrollment_status` to `active`. `AppContext` derives `state.weekNumber` — an absolute counter starting at 1 — from `getPersonalWeekNumber(curriculum_started_at)` (= `floor(days_since_start / 7) + 1`). The 26-week slot and cycle come from `getCurriculumWeek(n)` (`((n-1) % 26) + 1`) and `getCurriculumCycle(n)` (`floor((n-1)/26)+1`) in `curriculumService.js`. The cycle is **detached from the ISO calendar** — week 1 is simply the first 7 days after the user's start. After week 26 the cycle restarts at week 1 with the same content. `state.weekNumber` is `0` until the user enrolls.

> Do **not** reintroduce ISO-week-based scheduling or a shared `admin:current_week`. There is no global "current week" anymore — every employee has their own.

**Auto-Cancellation:** The PocketBase JS SDK has auto-cancellation enabled by default, which aborts concurrent identical requests (common under React StrictMode and `Promise.all`) with `ClientResponseError 0`. It is **globally disabled** via `pb.autoCancellation(false)` in `src/lib/pb.js`. Never re-enable it.

**PocketBase URL:** Resolved from `VITE_PB_URL`, else `window.location.origin` (`src/lib/pb.js`). In production Caddy proxies `/api/*` and `/_/*` to the PocketBase container.

**Important:** All `db.js` functions are `async`. Always `await` them — omitting `await` silently passes a Promise where data is expected.

## 3. The AI Integration (Anthropic)
All Anthropic calls go through one wrapper.
*   **Location:** `src/lib/llm.js`, function `callLLM(...)`. Callers must never reach `/api/anthropic` directly.
*   **Proxy:** Requests hit `/api/anthropic/v1/messages`. In Docker, Caddy proxies this to `https://api.anthropic.com` and injects the `x-api-key` header server-side. In local dev, `vite.config.js` does the same using `process.env.ANTHROPIC_API_KEY`. There is **no client-side API key**.
*   **Model tiers:** `fast` = `claude-haiku-4-5-20251001`, `standard` = `claude-sonnet-4-6`, `reasoning` = `claude-opus-4-7`. Choose a tier per task; admins can override per tier from Settings.
*   **Structured output:** Prefer Anthropic **tool use** with a forced `toolChoice`. Tool inputs are validated against Zod schemas in `src/lib/llmSchemas.js` (auto-looked-up via `toolSchemaRegistry`). For text responses, `parseStructuredText` strips code fences and extracts the outermost balanced JSON.
*   **Prompt caching:** Wrap stable system text with `cachedSystem(text)` to attach `cache_control: ephemeral`.
*   **Retry/limits:** `src/lib/llmRetry.js` handles exponential backoff with jitter on retryable statuses (408/425/429/5xx/529), honors `Retry-After`, and provides rate limiters (e.g. the extraction limiter caps ~20 req/min, burst 2). Default `maxTokens` is 4096; extraction and long-form content use 8192.
*   **Telemetry:** Every call is logged (best-effort, non-blocking) to the `llm_calls` collection.

## 4. Design System & Aesthetics
Respellion relies on a premium, modern aesthetic.
*   **CSS Variables:** Rely on the variables in `src/index.css` — `var(--color-bg)`, `var(--color-paper)`, `var(--color-teal)`, `var(--color-accent)`, radii `var(--r-sm)`, `var(--r-lg)`, `var(--r-org)`.
*   **Tailwind:** Tailwind v4 utilities are mapped to these variables. Use classes like `bg-teal`, `text-fg-muted`, `border-bg-warm`. Avoid raw hex codes.
*   **`stylesheet.css`** (repo root) is the authoritative visual reference and is frozen — do not edit it.
*   **Components:** Reuse the UI primitives in `src/components/ui/` (`Card.jsx`, `Button.jsx`, `Tag.jsx`, `Input.jsx`).

## 5. Learning Content Types (on-demand, per topic)
`src/lib/learningService.js` generates **three** long-form content types into the `content` collection, on demand:
| Type | Schema key | Description |
|---|---|---|
| `article` | `content.article` | Title, intro, sections, key takeaways |
| `slides` | `content.slides` | Slides with bullets and speaker notes |
| `infographic` | `content.infographic` | Headline, tagline, stats, steps, quote |

`generateLearningContent(topic, force, selectedType)` accepts one of the three types, or `'all'` (admin regeneration). Cache-hit logic checks `content[selectedType]` directly; new data is shallow-merged so other types are preserved. **There is no podcast type.**

Article refinement uses targeted patch tools (`set_intro`, `set_section`, `add_section`, `remove_section`, `replace_takeaways`) so the model edits only what changed.

## 6. Micro-Learnings (the weekly session)
`src/lib/microLearningService.js` generates short, single-topic interactions into the `micro_learnings` collection. **Three** types are currently active (a former `reflection_prompt` type was dropped):
| Type | Tier | Shape |
|---|---|---|
| `concept_explainer` | standard | `{ sections: [{ title, content (HTML) }] }` (≥3 sections) |
| `scenario_quiz` | standard | `{ scenario, options: [{ text, isCorrect, explanation }] }` (3–4 options, 1 correct) |
| `flashcard_set` | fast (Haiku) | `{ cards: [{ front, back }] }` (5–10 cards) |

`getOrGenerateMicroLearning(topicId, type)` returns a cached published record if present, else generates and stores one. Completions are recorded via `useMicroLearningCompletions` into `micro_learning_completions` (append-only). A weekly session is "done" when every required topic has at least one completed micro-learning.

## 7. Weekly Test
`src/lib/testService.js` builds a 5-question multiple-choice quiz for the user's current week.
*   Primary topic comes from the active curriculum week (else a deterministic hash fallback), plus a few review topics for breadth.
*   Generated in **one** `fast`-tier batch call (`emit_quiz_questions`), with quality gates: no duplicate options, no banned fillers ("all of the above"), explanations ≥20 chars, and a check that `correctIndex` isn't dominated by one position.
*   Scoring: **+2 points per correct answer** (`saveTestResult` → `score * 2`), written to the `leaderboard` collection.

## 8. Gamification
If you are extending gamification (`src/pages/Leaderboard.jsx`):
*   Tests grant **+2 points** per correct answer (`testService.js → saveTestResult`).
*   Badges are computed at render time: **First Steps** (1 test), **Veteran** (5 tests), **Perfectionist** (a 100% score).
*   Points accumulate in `leaderboard` via `db.upsertLeaderboardEntry`. Admins are excluded from the public board.

## 9. R42 Chatbot
The platform ships a global chatbot avatar called **R42**, rendered as the Respellion `{ r }` brand mark in three states (idle / typing / error).
*   **Mark component:** `src/components/ui/Mark.jsx` — pure SVG with `state`, `size`, `theme`, `brace`, `letter` props. Uses the `BallPill` font (`@font-face` in `src/index.css`), falling back to JetBrains Mono.
*   **Chat module:** `src/components/chat/`.
    *   `ChatLauncher.jsx` — global FAB; auto-hides when `quiz:active:{userId}` is set; listens to the `respellion:quiz-state` window event.
    *   `ChatWindow.jsx` — chat panel; renders messages from `useChat`; surfaces graph-delta confirmation chips.
    *   `useChat.js` — owns the message list, persists to `chat:thread:{userId}` (cap 50; only the last ~12 turns are sent to the API), calls `callLLM`.
    *   `prompts.js` — the cacheable system prompt blocks, greeting, and the `propose_graph_delta` tool spec (max 3 topics / 5 relations).
    *   `rag.js` — builds KB context using the TF-IDF index from `src/lib/retrieval.js` (top-K topics + verbatim mentions), filters relations to retrieved topics, and validates proposed deltas (dedupe by id/label, no orphan/self relations, hard caps).
*   **Model:** R42 runs on the `fast`/standard Claude tier (Haiku/Sonnet); low latency matters for chat.
*   **Quiz-integrity rule:** `src/pages/Testen.jsx` sets `quiz:active:{userId}=true` on start and clears it on every non-quiz phase + unmount, dispatching `respellion:quiz-state`. Never bypass this — letting users ask R42 mid-quiz would break scoring.
*   **Graph refinement:** when R42 proposes a `propose_graph_delta`, `rag.js` validates it and a confirmation chip appears inline. **Admin clicks Yes** → `kbStore.applyDelta` writes to PocketBase immediately. **Non-admin clicks Yes** → `kbStore.appendSuggestion` queues a `pending` entry in `kb:suggestions`.
*   **Admin approval UI:** `src/components/admin/SuggestionsQueue.jsx` lets admins approve (re-runs `applyDelta`) or reject queued suggestions.
*   **kbStore:** `src/lib/kbStore.js` is the single source of truth for chatbot-path KB mutations. It dispatches `respellion:kb-updated` after writes so the D3 graph and queue refresh.

## 10. Admin Panel
`src/pages/Admin/index.jsx` is tabbed: **Sources** (upload + extraction), **Content** (review/refine generated content), **Quizzes**, **Curriculum** (generate/preview/activate a 26-week schedule), **Graph** (D3 knowledge graph + suggestions queue), **Team** (manage members), **Settings** (model overrides, simulation toggle, smoke-test reset). Source upload lives in `src/components/admin/UploadZone.jsx` (`.txt` / `.md`, ≤5 MB).

## 11. How to Add New Features
1. **Schema:** add a PocketBase collection via the PB Admin UI or a migration in `pb_migrations/` (and mirror it in `scripts/setup-pb-collections.mjs`).
2. **DB helpers:** add async CRUD in `src/lib/db.js`.
3. **UI:** build with `Card`, `Button`, `Tag`, and Framer Motion entry animations.
4. **Logic:** connect to `src/store/AppContext.jsx` for global user/week context; otherwise keep state local.
5. **Verify:** `npm test`, `npm run lint`, `npm run build`.

## 12. Known Gotchas & Decisions
*   **Per-user curriculum start.** Week/cycle derive from each user's `curriculum_started_at`, not the calendar. There is no shared current week. New users are gated through `/onboarding` until enrolled.
*   **No podcast type.** Three content types only: `article`, `slides`, `infographic`.
*   **Three micro-learning types.** `concept_explainer`, `scenario_quiz`, `flashcard_set`. `reflection_prompt` was dropped.
*   **No Qdrant / no embeddings.** Retrieval is local TF-IDF (`src/lib/retrieval.js`).
*   **Caddy, not Nginx.** The reverse proxy is Caddy (`Caddyfile`).
*   **PocketBase auto-cancellation is OFF.** Set in `src/lib/pb.js`; never re-enable.
*   **Go through `callLLM`.** Never call the Anthropic proxy directly; you lose retry, schema validation, and telemetry.
*   **AI token budget.** Truncation surfaces as `LLMTruncatedError` (`stop_reason: max_tokens`). For extraction, tighten the prompt's topic cap before raising `max_tokens`.

## 13. 26-Week Per-User Curriculum System
The platform uses a **26-week perpetual curriculum cycle**. Every employee covers the knowledge base in focused, thematic weekly blocks, **starting whenever they enroll**.
*   **Service:** `src/lib/curriculumService.js` — week/cycle math, AI schedule generation, version lifecycle, progress.
*   **DB:** `curriculum_versions` holds generated JSON schedules; `topics` carry `theme`, `complexity_weight`, `difficulty` as generation input.
*   **Version lifecycle:** `draft` → `active` → `superseded`. Only one active version at a time (`CurriculumManager.jsx`).
*   **Per-user weeks:** `getPersonalWeekNumber(startedAt)` yields an absolute week counter; `getCurriculumWeek`/`getCurriculumCycle` map it to the 1–26 slot and cycle. Same content each cycle.
*   **Topic enrichment:** a one-off AI step assigns `theme`/`complexity_weight`/`difficulty` to topics missing them before generation (`enrichTopicsForCurriculum`, batches of 20).
*   **Progress:** `getYearProgress(userId, personalWeekNumber)` computes completion for the current cycle.
*   **Fallback:** if no active version exists, `getAssignedTopic()` falls back to deterministic hash-based assignment. Keep the fallback.

Good luck! You are building a platform that empowers continuous learning. Keep it fast, keep it beautiful, and keep the user engaged.