feat: phase 3 of AI pipeline hardening — extraction quality

Replace stateless one-shot extraction with a stateful, paced, cancellable
pipeline. Six subtasks:

- 3.1 Sentence-aware chunking with 800-char overlap (was paragraph-only
  at 4000 chars). Hard-split fallback for runaway sentences.
- 3.2 Stateful extraction: chunks 2+ receive an "already-extracted topic
  IDs" hint capped at 200 IDs, so the model reuses IDs instead of
  inventing variants like software-developer vs software-engineer.
- 3.3 Token-bucket limiter in llmRetry.js (extractionLimiter, 5 req/min).
  callLLM awaits the limiter before fetch; 429+Retry-After calls
  pauseUntil. Replaces hard setTimeout(12000) and setTimeout(15000).
- 3.4 relevance_locked column on topics — admin edits to relevance are
  sticky across re-extraction. Migration + merge respects the flag +
  unlock checkbox in KnowledgeGraph edit form.
- 3.5 Unify relation vocabulary — handbook prompt no longer mentions
  legacy "executes"; one-shot migration rewrites existing executes rows
  to executed_by with source/target swapped.
- 3.6 Cancellation — Cancel button on UploadZone wired to an
  AbortController threaded into callLLM; aborted runs persist status =
  "cancelled" rather than "failed".

Tests: 16 new unit tests for chunkText, buildKnownIdsHint, and
createLimiter. All 61 tests pass, 0 lint errors, build clean.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

eslint.config.js

@@ -14,7 +14,11 @@ export default defineConfig([
       reactRefresh.configs.vite,
     ],
     languageOptions: {
-      globals: globals.browser,
+      globals: {
+        ...globals.browser,
+        __BUILD_SHA__: 'readonly',
+        __BUILD_TIME__: 'readonly',
+      },
       parserOptions: { ecmaFeatures: { jsx: true } },
     },
     rules: {

pb_migrations/1780500000_updated_topics_relevance_locked.js

@@ -0,0 +1,23 @@
+/// <reference path="../pb_data/types.d.ts" />
+migrate((app) => {
+  const collection = app.findCollectionByNameOrId("pbc_2800040823")
+
+  // add field — relevance_locked is set to true whenever an admin edits
+  // learning_relevance via the UI; mergeKnowledgeGraph must never overwrite
+  // learning_relevance on a locked topic during re-extraction.
+  collection.fields.addAt(5, new Field({
+    "hidden": false,
+    "id": "bool_relevance_locked",
+    "name": "relevance_locked",
+    "presentable": false,
+    "required": false,
+    "system": false,
+    "type": "bool"
+  }))
+
+  return app.save(collection)
+}, (app) => {
+  const collection = app.findCollectionByNameOrId("pbc_2800040823")
+  collection.fields.removeById("bool_relevance_locked")
+  return app.save(collection)
+})

pb_migrations/1780500001_normalize_relation_types.js

@@ -0,0 +1,43 @@
+/// <reference path="../pb_data/types.d.ts" />
+// One-shot data migration: rewrite legacy "executes" relations to the
+// canonical "executed_by" vocabulary by swapping source and target.
+// Previously `role --executes--> process`; canonical is
+// `process --executed_by--> role`.
+migrate((app) => {
+  const records = app.findRecordsByFilter(
+    "pbc_1883724256",  // relations collection
+    'type = "executes"',
+    '',
+    0,
+    0,
+  )
+
+  for (const rec of records) {
+    const source = rec.get("source")
+    const target = rec.get("target")
+    rec.set("type", "executed_by")
+    rec.set("source", target)
+    rec.set("target", source)
+    app.save(rec)
+  }
+}, (app) => {
+  // Reverse: turn executed_by back into executes (best-effort — only those
+  // created before this migration would have been "executes"; rolling back
+  // will affect any newer executed_by rows too).
+  const records = app.findRecordsByFilter(
+    "pbc_1883724256",
+    'type = "executed_by"',
+    '',
+    0,
+    0,
+  )
+
+  for (const rec of records) {
+    const source = rec.get("source")
+    const target = rec.get("target")
+    rec.set("type", "executes")
+    rec.set("source", target)
+    rec.set("target", source)
+    app.save(rec)
+  }
+})

src/App.jsx

@@ -2,6 +2,7 @@ import { Routes, Route, Navigate, Link } from 'react-router-dom'
 import { BookOpen, CheckSquare, LayoutDashboard, Trophy, Settings, LogOut } from 'lucide-react'
 import { useApp } from './store/AppContext'
 import Mark from './components/ui/Mark'
+import BuildStamp from './components/ui/BuildStamp'
 import ChatLauncher from './components/chat/ChatLauncher'
 
 import Login from './pages/Login'
@@ -88,6 +89,7 @@ function App() {
   if (state.isLoading) return <div className="p-8 flex items-center justify-center min-h-screen">Loading application...</div>
 
   return (
+    <>
       <Routes>
         <Route path="/login" element={<Login />} />
         <Route path="/" element={<ProtectedRoute><Dashboard /></ProtectedRoute>} />
@@ -96,6 +98,8 @@ function App() {
         <Route path="/leaderboard" element={<ProtectedRoute><Leaderboard /></ProtectedRoute>} />
         <Route path="/admin/*" element={<ProtectedRoute requireAdmin={true}><Admin /></ProtectedRoute>} />
       </Routes>
+      <BuildStamp />
+    </>
   )
 }
 

src/components/admin/KnowledgeGraph.jsx

@@ -2,7 +2,8 @@ import { useCallback, useEffect, useRef, useState } from 'react';
 import * as d3 from 'd3';
 import { Trash2, Edit2, Save, X, RefreshCw, AlertCircle, Plus, Link as LinkIcon } from 'lucide-react';
 import * as db from '../../lib/db';
-import { anthropicApi } from '../../lib/api';
+import { callLLM } from '../../lib/llm';
+import { EMIT_GRAPH_ACTIONS_TOOL } from '../../lib/llmTools';
 import { analyzeHandbookDelta } from '../../lib/extractionPipeline';
 import { getRepoFolder, getFileContent } from '../../lib/githubService';
 import Button from '../ui/Button';
@@ -179,10 +180,17 @@ const KnowledgeGraph = () => {
   };
 
   const saveEdit = async () => {
-    await db.upsertTopic({ ...selectedNode, ...editData });
-    const updated = topics.map(t => t.id === selectedNode.id ? { ...t, ...editData } : t);
+    // If the admin touched learning_relevance, lock it so re-extraction won't overwrite the choice.
+    // But an explicit relevance_locked in editData (the unlock checkbox) always wins.
+    const relevanceChanged =
+      editData.learning_relevance !== undefined &&
+      editData.learning_relevance !== selectedNode.learning_relevance;
+    const next = { ...editData };
+    if (relevanceChanged && next.relevance_locked === undefined) next.relevance_locked = true;
+    await db.upsertTopic({ ...selectedNode, ...next });
+    const updated = topics.map(t => t.id === selectedNode.id ? { ...t, ...next } : t);
     setTopics(updated);
-    setSelectedNode({ ...selectedNode, ...editData });
+    setSelectedNode({ ...selectedNode, ...next });
     setIsEditing(false);
   };
 
@@ -264,22 +272,11 @@ const KnowledgeGraph = () => {
           setSyncProgress(`Processing ${count} of ${filesToProcess.length}: ${file.name}...`);
           try {
             const rawContent = await getFileContent('respellion', 'employee-handbook', file.path);
+            // Pacing is handled centrally by extractionLimiter inside analyzeHandbookDelta.
             await analyzeHandbookDelta(rawContent, file.path);
             await db.updateHandbookSyncState(file.path, file.sha);
-            
-            // To respect Anthropic's 5 requests per minute rate limit on this tier, 
-            // we pause for 15 seconds before processing the next file.
-            if (count < filesToProcess.length) {
-              setSyncProgress(`Waiting 15s to avoid rate limits... (${count}/${filesToProcess.length})`);
-              await new Promise(resolve => setTimeout(resolve, 15000));
-            }
           } catch (err) {
             console.error('Failed to process file:', file.path, err);
-            // We continue processing other files even if one fails, but still wait to avoid further rate limits
-            if (count < filesToProcess.length) {
-              setSyncProgress(`Error on ${file.name}. Waiting 15s... (${count}/${filesToProcess.length})`);
-              await new Promise(resolve => setTimeout(resolve, 15000));
-            }
           }
         }
         setSyncProgress('Sync Complete! Click "Analyze & Optimize Graph" above to clean up and merge.');
@@ -304,18 +301,18 @@ const KnowledgeGraph = () => {
       const currentTopics = await db.getTopics();
       const currentRelations = await db.getRelations();
 
-      const systemPrompt = `You are a strict Data Quality AI maintaining a Knowledge Graph.
-Your goal is to evaluate the provided topics and relations, identify duplicates to merge, useless nodes to delete, and new logical relations to add.
+      const systemPrompt = `You are a strict Data Quality AI maintaining a Knowledge Graph for Respellion.
+Evaluate the provided topics and relations and emit the actions to take via the emit_graph_actions tool.
 
 Rules:
-1. Identify topics that mean exactly the same thing. Choose one to keep, and one to delete.
-2. Identify topics that are too vague, irrelevant, or malformed to delete.
-3. Identify missing logical relations (depends_on, part_of, related_to) if two topics are conceptually linked but missing a relation.
-4. Evaluate the learning_relevance of each topic. If a topic is purely operational/mundane (like a printer guide), mark it as "exclude". If it's low priority, mark "peripheral".
-5. Return ONLY a valid JSON object describing the ACTIONS to take. Do not return the entire graph. Do not wrap in markdown blocks.`;
+1. Identify topics that mean exactly the same thing. Choose one to keep, one to delete (merges).
+2. Identify topics that are too vague, irrelevant, or malformed (deletions).
+3. Identify missing logical relations (depends_on, part_of, related_to, executed_by) between conceptually linked topics (newRelations).
+4. Evaluate learning_relevance. Mark purely operational topics (printer guides, etc.) as "exclude"; low-priority as "peripheral" (relevanceUpdates).
+
+Do not return the entire graph — only the actions to take.`;
 
       // Send a compact representation to minimize token usage and avoid rate limits.
-      // The AI only needs id, label, type, and relevance to identify duplicates/merges and adjust relevance.
       const compactTopics = currentTopics.map(({ id, label, type, learning_relevance }) => ({ id, label, type, learning_relevance }));
       const compactRelations = currentRelations.map(r => ({
         source: r.source?.id || r.source,
@@ -324,21 +321,20 @@ Rules:
       }));
 
       const userPrompt = `Here is the current knowledge graph:
-${JSON.stringify({ topics: compactTopics, relations: compactRelations })}
+${JSON.stringify({ topics: compactTopics, relations: compactRelations })}`;
 
-Analyze this graph and return ONLY the optimized JSON object with this EXACT structure:
-{
-  "merges": [ { "keepId": "id_to_keep", "deleteId": "id_to_delete" } ],
-  "deletions": [ "id_to_delete_completely" ],
-  "newRelations": [ { "source": "id1", "target": "id2", "type": "depends_on" } ],
-  "relevanceUpdates": [ { "id": "topic_id", "learning_relevance": "exclude" } ]
-}`;
+      const llmResult = await callLLM({
+        task: 'graph.analyze',
+        tier: 'reasoning',
+        system: [{ type: 'text', text: systemPrompt, cache_control: { type: 'ephemeral' } }],
+        user: userPrompt,
+        tools: [EMIT_GRAPH_ACTIONS_TOOL],
+        toolChoice: { type: 'tool', name: EMIT_GRAPH_ACTIONS_TOOL.name },
+        maxTokens: 4096,
+      });
 
-      const responseText = await anthropicApi.generateContent(systemPrompt, userPrompt);
-      const jsonMatch = responseText.match(/\{[\s\S]*\}/);
-      if (!jsonMatch) throw new Error('AI returned invalid format.');
-
-      const actions = JSON.parse(jsonMatch[0]);
+      const actions = llmResult.toolUses[0]?.input;
+      if (!actions) throw new Error('Graph analysis did not emit a tool result.');
 
       let updatedTopics = [...currentTopics];
       let updatedRelations = [...currentRelations];
@@ -369,7 +365,7 @@ Analyze this graph and return ONLY the optimized JSON object with this EXACT str
       if (actions.relevanceUpdates && Array.isArray(actions.relevanceUpdates)) {
         for (const update of actions.relevanceUpdates) {
           const topicIndex = updatedTopics.findIndex(t => t.id === update.id);
-          if (topicIndex !== -1) {
+          if (topicIndex !== -1 && !updatedTopics[topicIndex].relevance_locked) {
             updatedTopics[topicIndex] = { ...updatedTopics[topicIndex], learning_relevance: update.learning_relevance };
           }
         }
@@ -532,6 +528,17 @@ Analyze this graph and return ONLY the optimized JSON object with this EXACT str
                   <option value="peripheral">Peripheral</option>
                   <option value="exclude">Exclude</option>
                 </select>
+                {selectedNode.relevance_locked && (
+                  <label className="flex items-center gap-2 text-xs text-fg-muted mt-2 cursor-pointer">
+                    <input
+                      type="checkbox"
+                      checked={editData.relevance_locked !== false}
+                      onChange={e => setEditData({...editData, relevance_locked: e.target.checked})}
+                      className="rounded bg-bg-warm border-transparent focus:ring-0 text-teal"
+                    />
+                    Locked — re-extraction will not change this
+                  </label>
+                )}
               </div>
               <div>
                 <label className="text-xs text-fg-muted uppercase tracking-wider mb-1 block">Description</label>
@@ -554,9 +561,12 @@ Analyze this graph and return ONLY the optimized JSON object with this EXACT str
               </div>
               <div>
                 <p className="text-xs text-fg-muted uppercase tracking-wider mb-1">Type & Relevance</p>
-                <div className="flex gap-2">
+                <div className="flex gap-2 flex-wrap">
                   <span className="inline-block px-2 py-1 bg-bg-warm rounded-[var(--r-pill)] text-xs font-mono">{selectedNode.type}</span>
                   <span className="inline-block px-2 py-1 bg-bg-warm rounded-[var(--r-pill)] text-xs font-mono opacity-80">{selectedNode.learning_relevance || 'standard'}</span>
+                  {selectedNode.relevance_locked && (
+                    <span className="inline-block px-2 py-1 bg-teal/10 text-teal rounded-[var(--r-pill)] text-xs font-mono" title="Re-extraction will not change relevance for this topic.">locked</span>
+                  )}
                 </div>
               </div>
               <div>

src/components/admin/UploadZone.jsx

@@ -1,5 +1,5 @@
 import { useState, useRef } from 'react';
-import { UploadCloud, AlertCircle, CheckCircle } from 'lucide-react';
+import { UploadCloud, AlertCircle, CheckCircle, X } from 'lucide-react';
 import { processSourceText } from '../../lib/extractionPipeline';
 import Card from '../ui/Card';
 import Button from '../ui/Button';
@@ -12,24 +12,36 @@ const UploadZone = ({ onUploadComplete }) => {
   const [status, setStatus]                 = useState(null);
 
   const fileInputRef = useRef(null);
+  const abortRef     = useRef(null);
 
   // ── File upload (drag & drop / browse) ────────────────────────────────────
 
   const processFile = async (file) => {
     setIsProcessing(true);
     setStatus(null);
+    const controller = new AbortController();
+    abortRef.current = controller;
     try {
       const text = await file.text();
-      await processSourceText(text, file.name);
+      await processSourceText(text, file.name, { signal: controller.signal });
       setStatus({ type: 'success', msg: `Successfully processed: ${file.name}` });
       if (onUploadComplete) onUploadComplete();
     } catch (error) {
+      if (error?.name === 'AbortError') {
+        setStatus({ type: 'error', msg: `Cancelled: ${file.name}` });
+      } else {
         setStatus({ type: 'error', msg: `Error processing file: ${error.message}` });
+      }
     } finally {
+      abortRef.current = null;
       setIsProcessing(false);
     }
   };
 
+  const cancelProcessing = () => {
+    abortRef.current?.abort(new DOMException('Cancelled by user', 'AbortError'));
+  };
+
   const handleDragOver  = (e) => { e.preventDefault(); setIsDragging(true); };
   const handleDragLeave = ()  => setIsDragging(false);
 
@@ -80,6 +92,19 @@ const UploadZone = ({ onUploadComplete }) => {
         />
       </Card>
 
+      {/* Cancel sits outside the upload card so pointer-events-none doesn't disable it. */}
+      {isProcessing && (
+        <div className="flex justify-center">
+          <button
+            type="button"
+            onClick={cancelProcessing}
+            className="flex items-center gap-1 text-sm text-red-600 hover:text-red-700 underline"
+          >
+            <X size={14} /> Cancel extraction
+          </button>
+        </div>
+      )}
+
       {/* ─── Status messages ─── */}
       {status && (
         <div className={`p-4 rounded-[var(--r-sm)] flex items-start gap-3 ${

src/components/chat/prompts.js

@@ -23,10 +23,9 @@ export const STRINGS = {
   openAria: 'Open R42 chatbot',
 };
 
-export function buildSystemPrompt({ userName, isAdmin, kbContext }) {
-  return [
+const STABLE_PREAMBLE = [
   `Je bent R42, de chatbot-avatar van Respellion — een leerplatform voor microlearning, quizzen en kennisontwikkeling.`,
-    `Antwoord altijd in het Nederlands, kort en zakelijk-vriendelijk. Spreek de gebruiker aan met hun voornaam wanneer dat natuurlijk voelt (${userName}).`,
+  `Antwoord altijd in het Nederlands, kort en zakelijk-vriendelijk. Spreek de gebruiker aan met hun voornaam wanneer dat natuurlijk voelt.`,
   ``,
   `JE TAKEN:`,
   `1. Leg onderwerpen uit die in de kennisbasis staan.`,
@@ -34,9 +33,7 @@ export function buildSystemPrompt({ userName, isAdmin, kbContext }) {
   `3. Verwijs bij twijfel terug naar het bronmateriaal of zeg eerlijk dat je het niet weet.`,
   ``,
   `JE KENNIS:`,
-    `Je kennis is beperkt tot de onderstaande Respellion-kennisgraaf. Als een vraag duidelijk buiten dit bereik valt, zeg dat dan eerlijk en stel voor dat de gebruiker de bron toevoegt via Admin → Sources.`,
-    ``,
-    kbContext,
+  `Je kennis is beperkt tot de Respellion-kennisgraaf die hieronder volgt. Als een vraag duidelijk buiten dit bereik valt, zeg dat dan eerlijk en stel voor dat de gebruiker de bron toevoegt via Admin → Sources.`,
   ``,
   `KENNISGRAAF VERFIJNEN:`,
   `Wanneer de gebruiker iets noemt dat duidelijk een nieuw topic, nieuwe relatie, proces of rol is — en dat nog niet in de kennisgraaf staat — gebruik dan de tool "propose_graph_delta" om een voorstel te maken. Verzin niets: stel alleen iets voor als de gebruiker het concreet noemt. Stel maximaal 3 topics en 5 relaties per beurt voor.`,
@@ -45,10 +42,30 @@ export function buildSystemPrompt({ userName, isAdmin, kbContext }) {
   `- Houd antwoorden onder de 4 zinnen tenzij de gebruiker om uitleg vraagt.`,
   `- Geen markdown-headers; gewone Nederlandse tekst.`,
   `- Bij onzekerheid: "Ik weet het niet zeker — controleer dit in het handboek."`,
+].join('\n');
+
+/**
+ * Build the R42 system prompt as three cacheable blocks:
+ *   1. stable preamble (role, tasks, style)         — cached
+ *   2. KB context (current topics + relations)      — cached (hash-bust comes in Phase 5)
+ *   3. per-turn tail (user name + admin status)     — NOT cached
+ *
+ * Returning an array lets `callLLM` pass it through unchanged so the
+ * Anthropic API caches each block with the 5-minute ephemeral TTL.
+ */
+export function buildSystemPrompt({ userName, isAdmin, kbContext }) {
+  const tail = [
+    `De gebruiker heet ${userName}.`,
     isAdmin
-      ? `\nDe gebruiker is beheerder; voorstellen die de tool genereert worden direct toegepast.`
-      : `\nDe gebruiker is geen beheerder; voorstellen worden in een goedkeuringswachtrij gezet.`,
+      ? `De gebruiker is beheerder; voorstellen die de tool genereert worden direct toegepast.`
+      : `De gebruiker is geen beheerder; voorstellen worden in een goedkeuringswachtrij gezet.`,
   ].join('\n');
+
+  return [
+    { type: 'text', text: STABLE_PREAMBLE, cache_control: { type: 'ephemeral' } },
+    { type: 'text', text: kbContext, cache_control: { type: 'ephemeral' } },
+    { type: 'text', text: tail },
+  ];
 }
 
 export const PROPOSE_GRAPH_DELTA_TOOL = {

src/components/ui/BuildStamp.jsx

@@ -0,0 +1,19 @@
+const sha = typeof __BUILD_SHA__ === 'string' ? __BUILD_SHA__ : 'dev';
+const time = typeof __BUILD_TIME__ === 'string' ? __BUILD_TIME__ : new Date().toISOString();
+
+const formatted = (() => {
+  const d = new Date(time);
+  if (Number.isNaN(d.getTime())) return time;
+  return d.toLocaleString('nl-NL', { dateStyle: 'short', timeStyle: 'short' });
+})();
+
+const BuildStamp = () => (
+  <div
+    className="hidden md:block fixed bottom-1 right-2 text-[10px] font-mono text-fg-muted/60 z-40 pointer-events-none select-none"
+    title={`Build ${sha} at ${time}`}
+  >
+    v{sha} · {formatted}
+  </div>
+);
+
+export default BuildStamp;

src/lib/__tests__/articlePatches.test.js

@@ -0,0 +1,104 @@
+import { describe, expect, it } from 'vitest';
+import { applyArticlePatches, applyAndValidate } from '../articlePatches';
+
+const article = () => ({
+  title: 'Onboarding',
+  intro: 'Old intro.',
+  sections: [
+    { heading: 'Day one', body: 'First day body, three sentences long. Welcome. Read the handbook.' },
+    { heading: 'Day two', body: 'Second day body. Three sentences. Meet your team.' },
+  ],
+  keyTakeaways: ['Show up', 'Ask questions'],
+});
+
+describe('applyArticlePatches', () => {
+  it('does not mutate the input article', () => {
+    const original = article();
+    const snapshot = JSON.parse(JSON.stringify(original));
+    applyArticlePatches(original, [
+      { name: 'set_intro', input: { intro: 'New intro.' } },
+    ]);
+    expect(original).toEqual(snapshot);
+  });
+
+  it('set_intro replaces the intro', () => {
+    const result = applyArticlePatches(article(), [
+      { name: 'set_intro', input: { intro: 'Punchier intro.' } },
+    ]);
+    expect(result.intro).toBe('Punchier intro.');
+  });
+
+  it('set_section replaces the matching section body (case-insensitive)', () => {
+    const result = applyArticlePatches(article(), [
+      { name: 'set_section', input: { heading: 'DAY ONE', body: 'Rewritten body. With several sentences. Indeed.' } },
+    ]);
+    expect(result.sections[0].body).toMatch(/Rewritten body/);
+    expect(result.sections[1].body).toMatch(/Second day body/);
+  });
+
+  it('add_section position=start prepends a new section', () => {
+    const result = applyArticlePatches(article(), [
+      { name: 'add_section', input: { heading: 'Before', body: 'New intro section. Three sentences. Indeed.', position: 'start' } },
+    ]);
+    expect(result.sections[0].heading).toBe('Before');
+    expect(result.sections).toHaveLength(3);
+  });
+
+  it('add_section position=end appends a new section', () => {
+    const result = applyArticlePatches(article(), [
+      { name: 'add_section', input: { heading: 'After', body: 'Closing section. Three sentences. Indeed.', position: 'end' } },
+    ]);
+    expect(result.sections[2].heading).toBe('After');
+  });
+
+  it('remove_section drops the matching section', () => {
+    const result = applyArticlePatches(article(), [
+      { name: 'remove_section', input: { heading: 'Day one' } },
+    ]);
+    expect(result.sections).toHaveLength(1);
+    expect(result.sections[0].heading).toBe('Day two');
+  });
+
+  it('replace_takeaways swaps the key takeaways', () => {
+    const result = applyArticlePatches(article(), [
+      { name: 'replace_takeaways', input: { items: ['First', 'Second', 'Third'] } },
+    ]);
+    expect(result.keyTakeaways).toEqual(['First', 'Second', 'Third']);
+  });
+
+  it('applies multiple patches in order', () => {
+    const result = applyArticlePatches(article(), [
+      { name: 'set_intro', input: { intro: 'Brand new intro.' } },
+      { name: 'remove_section', input: { heading: 'Day one' } },
+      { name: 'add_section', input: { heading: 'New', body: 'Body of the new section. Three sentences. Yes.', position: 'end' } },
+    ]);
+    expect(result.intro).toBe('Brand new intro.');
+    expect(result.sections.map(s => s.heading)).toEqual(['Day two', 'New']);
+  });
+
+  it('falls back to appending when set_section cannot find a matching heading', () => {
+    const result = applyArticlePatches(article(), [
+      { name: 'set_section', input: { heading: 'Nonexistent', body: 'New body, with three sentences. Yes indeed. Foo.' } },
+    ]);
+    expect(result.sections).toHaveLength(3);
+    expect(result.sections[2].heading).toBe('Nonexistent');
+  });
+});
+
+describe('applyAndValidate', () => {
+  it('returns the patched article when valid', () => {
+    const patched = applyAndValidate(article(), [
+      { name: 'set_intro', input: { intro: 'Tighter intro.' } },
+    ]);
+    expect(patched.intro).toBe('Tighter intro.');
+  });
+
+  it('throws when patches strip the article to invalid', () => {
+    expect(() =>
+      applyAndValidate(article(), [
+        { name: 'remove_section', input: { heading: 'Day one' } },
+        { name: 'remove_section', input: { heading: 'Day two' } },
+      ]),
+    ).toThrow(/invalid article/i);
+  });
+});

src/lib/__tests__/extractionPipeline.test.js

@@ -0,0 +1,89 @@
+import { describe, expect, it, vi, beforeEach, afterEach } from 'vitest';
+
+vi.mock('../pb', () => ({
+  pb: { collection: () => ({ create: () => ({ catch: () => {} }) }) },
+}));
+
+import { chunkText, buildKnownIdsHint, MAX_CHUNK_CHARS, OVERLAP_CHARS } from '../extractionPipeline';
+
+describe('chunkText', () => {
+  let warnSpy;
+  beforeEach(() => { warnSpy = vi.spyOn(console, 'warn').mockImplementation(() => {}); });
+  afterEach(() => { warnSpy.mockRestore(); });
+
+  it('returns the original text as a single chunk when below maxChars', () => {
+    const result = chunkText('A short paragraph.');
+    expect(result).toEqual(['A short paragraph.']);
+  });
+
+  it('returns empty array for empty/whitespace input', () => {
+    expect(chunkText('')).toEqual([]);
+    expect(chunkText('   \n  ')).toEqual([]);
+  });
+
+  it('splits along sentence boundaries with overlap between adjacent chunks', () => {
+    const sentence = 'This is a sentence with exactly a known length. ';
+    const text = sentence.repeat(100); // ~5000 chars
+    const chunks = chunkText(text, { maxChars: 600, overlapChars: 150 });
+
+    expect(chunks.length).toBeGreaterThan(1);
+    for (const c of chunks) {
+      expect(c.length).toBeLessThanOrEqual(600);
+    }
+    // Adjacent chunks share trailing text — the overlap should be non-empty.
+    for (let i = 1; i < chunks.length; i++) {
+      const tail = chunks[i - 1].slice(-150);
+      // The new chunk must begin with content that appears at the tail of the prior chunk.
+      const firstHundred = chunks[i].slice(0, 100);
+      // At least one word from the tail should appear in the head of the next chunk.
+      const words = tail.split(/\s+/).filter((w) => w.length > 3);
+      const shared = words.some((w) => firstHundred.includes(w));
+      expect(shared).toBe(true);
+    }
+  });
+
+  it('hard-splits a single sentence that exceeds maxChars and logs a warning', () => {
+    const huge = 'word '.repeat(400).trim() + '.'; // ~2000 chars, no sentence break
+    const chunks = chunkText(huge, { maxChars: 500, overlapChars: 50 });
+    expect(chunks.length).toBeGreaterThan(1);
+    expect(warnSpy).toHaveBeenCalled();
+    for (const c of chunks) expect(c.length).toBeLessThanOrEqual(500);
+  });
+
+  it('handles paragraph-only splits when no sentence punctuation is present', () => {
+    const paragraphs = Array.from({ length: 10 }, (_, i) => `paragraph ${i} content here`).join('\n\n');
+    const chunks = chunkText(paragraphs, { maxChars: 80, overlapChars: 20 });
+    expect(chunks.length).toBeGreaterThan(1);
+  });
+
+  it('uses the documented defaults', () => {
+    expect(MAX_CHUNK_CHARS).toBe(8000);
+    expect(OVERLAP_CHARS).toBe(800);
+  });
+});
+
+describe('buildKnownIdsHint', () => {
+  it('returns empty string when no IDs are known', () => {
+    expect(buildKnownIdsHint([])).toBe('');
+    expect(buildKnownIdsHint(undefined)).toBe('');
+    expect(buildKnownIdsHint(null)).toBe('');
+  });
+
+  it('formats the known IDs as a bulleted list with a leading instruction', () => {
+    const hint = buildKnownIdsHint(['software-engineer', 'onboarding-buddy']);
+    expect(hint).toContain('Already-extracted topic IDs');
+    expect(hint).toContain('- software-engineer');
+    expect(hint).toContain('- onboarding-buddy');
+    expect(hint.endsWith('\n')).toBe(true);
+  });
+
+  it('caps the hint at the 200 most recent IDs', () => {
+    const ids = Array.from({ length: 250 }, (_, i) => `topic-${i}`);
+    const hint = buildKnownIdsHint(ids);
+    // The newest IDs must appear; the oldest must not.
+    expect(hint).toContain('topic-249');
+    expect(hint).toContain('topic-50');
+    expect(hint).not.toContain('topic-49');
+    expect(hint).not.toContain('topic-0\n');
+  });
+});

src/lib/__tests__/llmRetry.test.js

@@ -0,0 +1,72 @@
+import { describe, expect, it, vi, afterEach } from 'vitest';
+import { createLimiter, extractionLimiter } from '../llmRetry';
+
+afterEach(() => { vi.useRealTimers(); });
+
+describe('createLimiter', () => {
+  it('rejects an invalid rps', () => {
+    expect(() => createLimiter({ rps: 0 })).toThrow();
+    expect(() => createLimiter({ rps: -1 })).toThrow();
+  });
+
+  it('rejects an invalid burst', () => {
+    expect(() => createLimiter({ rps: 1, burst: 0 })).toThrow();
+  });
+
+  it('lets the first call through immediately (initial burst token)', async () => {
+    const limiter = createLimiter({ rps: 1, burst: 1 });
+    const start = Date.now();
+    await limiter.acquire();
+    expect(Date.now() - start).toBeLessThan(50);
+  });
+
+  it('queues subsequent calls to respect the spacing', async () => {
+    vi.useFakeTimers();
+    const limiter = createLimiter({ rps: 10, burst: 1 }); // 100ms spacing
+    await limiter.acquire(); // consume initial token
+
+    let resolved = false;
+    const p = limiter.acquire().then(() => { resolved = true; });
+
+    await vi.advanceTimersByTimeAsync(50);
+    expect(resolved).toBe(false);
+
+    await vi.advanceTimersByTimeAsync(100);
+    await p;
+    expect(resolved).toBe(true);
+  });
+
+  it('honours pauseUntil — no acquire returns before the pause expires', async () => {
+    vi.useFakeTimers();
+    const limiter = createLimiter({ rps: 100, burst: 5 });
+    limiter.pauseUntil(Date.now() + 1000);
+
+    let resolved = false;
+    const p = limiter.acquire().then(() => { resolved = true; });
+
+    await vi.advanceTimersByTimeAsync(500);
+    expect(resolved).toBe(false);
+
+    await vi.advanceTimersByTimeAsync(600);
+    await p;
+    expect(resolved).toBe(true);
+  });
+
+  it('aborts a queued acquire when the signal fires', async () => {
+    const limiter = createLimiter({ rps: 1, burst: 1 });
+    await limiter.acquire(); // consume
+
+    const ctl = new AbortController();
+    const p = limiter.acquire({ signal: ctl.signal });
+    ctl.abort();
+
+    await expect(p).rejects.toBeInstanceOf(DOMException);
+  });
+});
+
+describe('extractionLimiter', () => {
+  it('is exported and exposes the limiter shape', () => {
+    expect(typeof extractionLimiter.acquire).toBe('function');
+    expect(typeof extractionLimiter.pauseUntil).toBe('function');
+  });
+});

src/lib/__tests__/llmTools.test.js

@@ -0,0 +1,44 @@
+import { describe, expect, it } from 'vitest';
+import {
+  EMIT_KNOWLEDGE_GRAPH_TOOL,
+  EMIT_HANDBOOK_DELTA_TOOL,
+  EMIT_LEARNING_ARTICLE_TOOL,
+  EMIT_LEARNING_SLIDES_TOOL,
+  EMIT_LEARNING_INFOGRAPHIC_TOOL,
+  EMIT_LEARNING_ALL_TOOL,
+  EMIT_CUSTOM_TOPIC_TOOL,
+  EMIT_QUIZ_QUESTIONS_TOOL,
+  EMIT_GRAPH_ACTIONS_TOOL,
+  ARTICLE_PATCH_TOOLS,
+} from '../llmTools';
+import { toolSchemaRegistry } from '../llmSchemas';
+
+const allTools = [
+  EMIT_KNOWLEDGE_GRAPH_TOOL,
+  EMIT_HANDBOOK_DELTA_TOOL,
+  EMIT_LEARNING_ARTICLE_TOOL,
+  EMIT_LEARNING_SLIDES_TOOL,
+  EMIT_LEARNING_INFOGRAPHIC_TOOL,
+  EMIT_LEARNING_ALL_TOOL,
+  EMIT_CUSTOM_TOPIC_TOOL,
+  EMIT_QUIZ_QUESTIONS_TOOL,
+  EMIT_GRAPH_ACTIONS_TOOL,
+  ...ARTICLE_PATCH_TOOLS,
+];
+
+describe('llmTools', () => {
+  it('every tool has a name, description, and object input_schema', () => {
+    for (const t of allTools) {
+      expect(typeof t.name).toBe('string');
+      expect(t.name.length).toBeGreaterThan(0);
+      expect(typeof t.description).toBe('string');
+      expect(t.input_schema).toMatchObject({ type: 'object' });
+    }
+  });
+
+  it('every tool has a matching Zod validator in toolSchemaRegistry', () => {
+    for (const t of allTools) {
+      expect(toolSchemaRegistry[t.name]).toBeTruthy();
+    }
+  });
+});

src/lib/articlePatches.js

@@ -0,0 +1,80 @@
+/**
+ * Apply a sequence of patch operations (the tool_use calls returned by
+ * `refineLearningContent`) to an article object, in order. The returned
+ * article is a fresh object — the input is not mutated.
+ *
+ * Recognised tool names mirror `llmTools.js`:
+ *   set_intro, set_section, add_section, remove_section, replace_takeaways.
+ *
+ * Unknown tool names are ignored on purpose; the caller validates the
+ * result against `learningArticleSchema` and rejects the whole turn if
+ * the patches produced an invalid article.
+ */
+
+import { learningArticleSchema } from './llmSchemas';
+
+function matchesHeading(section, heading) {
+  return (section.heading ?? '').trim().toLowerCase() === heading.trim().toLowerCase();
+}
+
+function cloneArticle(article) {
+  return {
+    ...article,
+    sections: article.sections.map((s) => ({ ...s })),
+    keyTakeaways: [...article.keyTakeaways],
+  };
+}
+
+export function applyArticlePatches(article, toolUses) {
+  let next = cloneArticle(article);
+  for (const tu of toolUses) {
+    switch (tu.name) {
+      case 'set_intro':
+        next = { ...next, intro: tu.input.intro };
+        break;
+      case 'set_section': {
+        const idx = next.sections.findIndex((s) => matchesHeading(s, tu.input.heading));
+        if (idx === -1) {
+          // No matching section — fall back to appending so the model's
+          // intent (provide that body) is preserved rather than lost.
+          next.sections = [...next.sections, { heading: tu.input.heading, body: tu.input.body }];
+        } else {
+          next.sections = next.sections.map((s, i) => (i === idx ? { ...s, body: tu.input.body } : s));
+        }
+        break;
+      }
+      case 'add_section': {
+        const newSection = { heading: tu.input.heading, body: tu.input.body };
+        next.sections = tu.input.position === 'start'
+          ? [newSection, ...next.sections]
+          : [...next.sections, newSection];
+        break;
+      }
+      case 'remove_section':
+        next.sections = next.sections.filter((s) => !matchesHeading(s, tu.input.heading));
+        break;
+      case 'replace_takeaways':
+        next = { ...next, keyTakeaways: [...tu.input.items] };
+        break;
+      default:
+        // Unknown patch op — ignore.
+        break;
+    }
+  }
+  return next;
+}
+
+/**
+ * Apply the patches and re-validate against the article schema. Throws
+ * a clear error if the result is invalid.
+ */
+export function applyAndValidate(article, toolUses) {
+  const updated = applyArticlePatches(article, toolUses);
+  const parsed = learningArticleSchema.safeParse({ article: updated });
+  if (!parsed.success) {
+    const err = new Error(`Refinement produced an invalid article: ${parsed.error.message}`);
+    err.cause = parsed.error;
+    throw err;
+  }
+  return parsed.data.article;
+}

src/lib/db.js

@@ -18,6 +18,7 @@ export async function saveTopics(topics) {
       type: t.type,
       description: t.description,
       learning_relevance: t.learning_relevance || 'standard',
+      relevance_locked: t.relevance_locked === true,
     }, { requestKey: null });
   }
 }

src/lib/extractionPipeline.js

@@ -1,104 +1,157 @@
-import { anthropicApi } from './api';
 import * as db from './db';
+import { callLLM } from './llm';
+import { extractionLimiter } from './llmRetry';
+import { EMIT_KNOWLEDGE_GRAPH_TOOL, EMIT_HANDBOOK_DELTA_TOOL } from './llmTools';
+import { normalizeHandbookResult } from './llmSchemas';
 
-const SYSTEM_PROMPT = `You are an AI knowledge extractor for Respellion, an IT company built on radical transparency.
-You receive a source text. Your task is to extract all core concepts, roles, and processes from the text, and return them as a structured JSON Knowledge Graph.
-Facts should be integrated into the descriptions of the other labels and NOT be extracted as unique topics.
+const MAX_KNOWN_IDS_HINT = 200;
+
+/**
+ * Build the "already-extracted topic IDs" hint that prepends every chunk
+ * after the first. Capped at the most-recent `MAX_KNOWN_IDS_HINT` IDs so
+ * the prompt stays a bounded size; the model uses this list to reuse IDs
+ * rather than invent variants like `software-developer` for
+ * `software-engineer`.
+ */
+export function buildKnownIdsHint(ids) {
+  if (!ids || !ids.length) return '';
+  const recent = ids.slice(-MAX_KNOWN_IDS_HINT);
+  return [
+    'Already-extracted topic IDs (do NOT create new IDs for these — reuse them if the same concept appears here):',
+    ...recent.map((id) => `- ${id}`),
+    '',
+  ].join('\n');
+}
+
+const EXTRACTION_SYSTEM_PROMPT = `You are an AI knowledge extractor for Respellion, an IT company built on radical transparency.
+You receive a source text. Extract every distinct concept, role, and process from it and emit them through the emit_knowledge_graph tool.
 
 CRITICAL INSTRUCTIONS FOR COMPLETENESS:
-- You must extract EVERY SINGLE distinct role, process, and concept described or mentioned in the source text. 
-- DO NOT summarize, skip, truncate, or omit any items. 
-- If the document contains 29 roles, your JSON topics array must contain exactly 29 role topics. 
-- Completeness is of paramount importance. Failing to extract all topics will result in loss of critical company knowledge.
-- Keep descriptions concise (max 3 sentences) to ensure you have enough output tokens to list everything.
+- Extract EVERY SINGLE distinct role, process, and concept described or mentioned in the source text.
+- DO NOT summarise, skip, truncate, or omit any items.
+- If the document contains 29 roles, the topics array must contain exactly 29 role topics.
+- Completeness is paramount. Failing to extract all topics loses critical company knowledge.
+- Facts should be integrated into the descriptions of other topics — never extracted as standalone topics.
+- Keep descriptions concise (max 3 sentences) so the response fits.
 
-You MUST assign a learning_relevance to each topic:
-- "core": Fundamental company knowledge.
-- "standard": Normal learning topics.
-- "peripheral": Good to know, but low priority.
-- "exclude": Pure operational reference material (e.g., printer guides, wifi passwords) that should NEVER be tested.
+Topic IDs are lowercase kebab-case slugs specific to the topic (e.g. "software-engineer", "data-quality-review"). Do not use generic IDs like "role-1" or "concept-2".
 
-ALWAYS return a valid JSON object in the following format:
-{
-  "topics": [
-    {
-      "id": "a-unique-lowercase-kebab-case-slug-specific-to-this-topic (e.g., 'software-engineer' or 'data-quality-review'). DO NOT use generic IDs like 'role-1' or 'concept-2'.",
-      "label": "Topic title",
-      "type": "concept | role | process",
-      "description": "A concise, clear explanation of max 3 sentences.",
-      "learning_relevance": "core | standard | peripheral | exclude"
-    }
-  ],
-  "relations": [
-    {
-      "source": "topic-id-1",
-      "target": "topic-id-2",
-      "type": "related_to | depends_on | part_of | executed_by"
-    }
-  ]
-}
-Return JSON only. No markdown blocks or other text.`;
+Assign a learning_relevance to every topic:
+- "core": fundamental company knowledge.
+- "standard": normal learning topics.
+- "peripheral": good to know, low priority.
+- "exclude": pure operational reference (printer guides, wifi passwords) that should never be tested.
 
-const HANDBOOK_SYSTEM_PROMPT = `You are analyzing an update to the Respellion Employee Handbook.
-Your task is to identify changes and extract structural knowledge.
+Relation types: related_to | depends_on | part_of | executed_by.
+`;
 
-CRITICAL INSTRUCTION:
-You must explicitly identify and create relations between Roles, Processes, and Concepts.
-Every Process must have a Role attached (who does it).
-Every Concept must have a relation to a Process or Role.
+const HANDBOOK_SYSTEM_PROMPT = `You are analysing an update to the Respellion Employee Handbook. Emit the extracted topics and relations through the emit_handbook_delta tool.
 
-You MUST assign a learning_relevance to each topic:
-- "core": Fundamental company knowledge.
-- "standard": Normal learning topics.
-- "peripheral": Good to know, but low priority.
-- "exclude": Pure operational reference material (e.g., printer guides, wifi passwords) that should NEVER be tested.
+CRITICAL INSTRUCTIONS:
+- Every process must have a role attached. Express this as: process --executed_by--> role.
+- Every concept must connect to a process or role.
+- Mark handbook topics with metadata.source = "github_handbook".
+- Assign learning_relevance using the same scale as extraction: core | standard | peripheral | exclude.
 
-Return a JSON object:
-{
-  "topics": [
-    { "id": "...", "label": "...", "type": "role | process | concept", "description": "...", "learning_relevance": "standard", "metadata": { "source": "github_handbook" } }
-  ],
-  "relations": [
-    { "source": "role-id", "target": "process-id", "type": "executes | related_to | depends_on | part_of", "description": "Brief metadata about this specific relation" }
-  ]
-}
-Return JSON only. No markdown blocks or other text.`;
+Relation types: related_to | depends_on | part_of | executed_by.
+`;
 
-export async function analyzeHandbookDelta(fileContent, filePath) {
-  const responseText = await anthropicApi.generateContent(HANDBOOK_SYSTEM_PROMPT, `Analyze the following handbook file update (${filePath}):\n\n${fileContent}`);
+const cachedSystem = (text) => [{ type: 'text', text, cache_control: { type: 'ephemeral' } }];
 
-  let extractedData;
-  try {
-    const jsonMatch = responseText.match(/\{[\s\S]*\}/);
-    const jsonStr = jsonMatch ? jsonMatch[0] : responseText;
-    extractedData = JSON.parse(jsonStr);
-  } catch (e) {
-    console.error('[Pipeline] AI returned non-JSON response for handbook delta:', responseText?.substring(0, 500));
-    throw new Error(`AI response was not valid JSON. The model responded with: "${responseText?.substring(0, 120)}..."`, { cause: e });
-  }
+export async function analyzeHandbookDelta(fileContent, filePath, { signal } = {}) {
+  const result = await callLLM({
+    task: 'extract.handbook',
+    tier: 'standard',
+    system: cachedSystem(HANDBOOK_SYSTEM_PROMPT),
+    user: `Analyze the following handbook file update (${filePath}):\n\n${fileContent}`,
+    tools: [EMIT_HANDBOOK_DELTA_TOOL],
+    toolChoice: { type: 'tool', name: EMIT_HANDBOOK_DELTA_TOOL.name },
+    maxTokens: 8192,
+    limiter: extractionLimiter,
+    signal,
+  });
+
+  const raw = result.toolUses[0]?.input;
+  if (!raw) throw new Error('Handbook extraction did not emit a tool result.');
+  const extractedData = normalizeHandbookResult(raw);
 
   await mergeKnowledgeGraph(extractedData);
   return { success: true, data: extractedData };
 }
-function chunkText(text, maxChunkSize = 4000) {
-  const paragraphs = text.split(/\n+/);
-  const chunks = [];
-  let currentChunk = '';
 
-  for (const para of paragraphs) {
-    if ((currentChunk + '\n' + para).length > maxChunkSize) {
-      if (currentChunk) chunks.push(currentChunk.trim());
-      currentChunk = para;
-    } else {
-      currentChunk = currentChunk ? currentChunk + '\n' + para : para;
+/**
+ * Sentence-aware chunker with overlap.
+ *
+ * Targets ~2000 input tokens per chunk (`MAX_CHUNK_CHARS / 4`). Splits on
+ * sentence boundaries first, then falls back to paragraph boundaries, and
+ * hard-splits inside an oversized sentence as a last resort. Adjacent chunks
+ * share `overlapChars` of trailing text to preserve cross-boundary context
+ * for the model.
+ *
+ * Exported for unit tests; callers in this module use it directly.
+ *
+ * @param {string} text
+ * @param {{ maxChars?: number, overlapChars?: number }} [opts]
+ * @returns {string[]}
+ */
+export const MAX_CHUNK_CHARS = 8000;
+export const OVERLAP_CHARS = 800;
+
+export function chunkText(text, { maxChars = MAX_CHUNK_CHARS, overlapChars = OVERLAP_CHARS } = {}) {
+  if (typeof text !== 'string' || !text.trim()) return [];
+  const trimmed = text.trim();
+  if (trimmed.length <= maxChars) return [trimmed];
+
+  const units = splitIntoChunkableUnits(trimmed, maxChars);
+  if (units.length === 0) return [];
+
+  const chunks = [];
+  let buf = '';
+  let bufLen = 0; // length of new (non-overlap) content added since last flush
+
+  for (const unit of units) {
+    const wouldOverflow = (buf ? buf.length + 1 + unit.length : unit.length) > maxChars;
+    if (wouldOverflow && bufLen > 0) {
+      chunks.push(buf.trim());
+      const overlap = buf.length > overlapChars ? buf.slice(-overlapChars) : '';
+      buf = overlap;
+      bufLen = 0;
     }
+    // If the overlap + unit still won't fit, drop the overlap so the unit fits cleanly.
+    if (buf && (buf.length + 1 + unit.length) > maxChars) {
+      buf = '';
     }
-  if (currentChunk) chunks.push(currentChunk.trim());
+    buf = buf ? buf + ' ' + unit : unit;
+    bufLen += unit.length + (bufLen > 0 ? 1 : 0);
+  }
+  if (bufLen > 0 && buf.trim()) chunks.push(buf.trim());
   return chunks;
 }
 
-export async function processSourceText(textContent, sourceName) {
-  // Deduplicate: skip if a source with the same name was already successfully processed
+function splitIntoChunkableUnits(text, maxChars) {
+  const paragraphs = text.split(/\n\s*\n+/);
+  const units = [];
+  for (const para of paragraphs) {
+    const trimmedPara = para.trim();
+    if (!trimmedPara) continue;
+    const sentences = trimmedPara.split(/(?<=[.!?])\s+/);
+    for (const s of sentences) {
+      const sentence = s.trim();
+      if (!sentence) continue;
+      if (sentence.length <= maxChars) {
+        units.push(sentence);
+      } else {
+        for (let i = 0; i < sentence.length; i += maxChars) {
+          units.push(sentence.slice(i, i + maxChars));
+        }
+        console.warn(`[chunkText] Hard-split a sentence of ${sentence.length} chars (exceeds maxChars=${maxChars}).`);
+      }
+    }
+  }
+  return units;
+}
+
+export async function processSourceText(textContent, sourceName, { signal } = {}) {
   const existing = await db.getSources();
   const alreadyDone = existing.find(
     s => s.name === sourceName && s.status === 'completed'
@@ -111,51 +164,54 @@ export async function processSourceText(textContent, sourceName) {
   const sourceId = rec.id;
 
   try {
-    const chunks = chunkText(textContent, 4000);
+    const chunks = chunkText(textContent);
     console.log(`[Pipeline] Split "${sourceName}" into ${chunks.length} chunks for processing.`);
 
-    let allExtractedTopics = [];
-    let allExtractedRelations = [];
+    const existingTopics = await db.getTopics();
+    const knownIds = existingTopics.map((t) => t.id);
+
+    const allExtractedTopics = [];
+    const allExtractedRelations = [];
 
     for (let i = 0; i < chunks.length; i++) {
-      if (i > 0) {
-        console.log(`[Pipeline] Pacing delay (12s) to prevent rate limits before chunk ${i + 1}/${chunks.length}...`);
-        await new Promise(r => setTimeout(r, 12000));
-      }
+      if (signal?.aborted) throw signal.reason ?? new DOMException('Aborted', 'AbortError');
 
       console.log(`[Pipeline] Processing chunk ${i + 1}/${chunks.length} (${chunks[i].length} chars)...`);
-      const responseText = await anthropicApi.generateContent(
-        SYSTEM_PROMPT, 
-        `Analyze this part of the document (${i + 1}/${chunks.length}):\n\n${chunks[i]}`
-      );
-      console.log(`[Pipeline] Raw AI response for chunk ${i + 1}:`, responseText);
+      const hint = i > 0 ? buildKnownIdsHint(knownIds) : '';
+      const result = await callLLM({
+        task: 'extract.source',
+        tier: 'standard',
+        system: cachedSystem(EXTRACTION_SYSTEM_PROMPT),
+        user: `${hint}Analyze this part of the document (${i + 1}/${chunks.length}):\n\n${chunks[i]}`,
+        tools: [EMIT_KNOWLEDGE_GRAPH_TOOL],
+        toolChoice: { type: 'tool', name: EMIT_KNOWLEDGE_GRAPH_TOOL.name },
+        maxTokens: 8192,
+        limiter: extractionLimiter,
+        signal,
+      });
 
-      let extractedData;
-      try {
-        const jsonMatch = responseText.match(/\{[\s\S]*\}/);
-        const jsonStr = jsonMatch ? jsonMatch[0] : responseText;
-        extractedData = JSON.parse(jsonStr);
-      } catch (e) {
-        console.error(`[Pipeline] AI returned non-JSON response for chunk ${i + 1}:`, responseText?.substring(0, 500));
-        throw new Error(`AI response for chunk ${i + 1} was not valid JSON.`, { cause: e });
-      }
+      const extractedData = result.toolUses[0]?.input;
+      if (!extractedData) throw new Error(`Extraction did not emit a tool result for chunk ${i + 1}.`);
 
-      if (extractedData.topics && Array.isArray(extractedData.topics)) {
+      if (Array.isArray(extractedData.topics)) {
         allExtractedTopics.push(...extractedData.topics);
+        for (const t of extractedData.topics) {
+          if (t?.id && !knownIds.includes(t.id)) knownIds.push(t.id);
         }
-      if (extractedData.relations && Array.isArray(extractedData.relations)) {
+      }
+      if (Array.isArray(extractedData.relations)) {
         allExtractedRelations.push(...extractedData.relations);
       }
     }
 
-    // Merge everything together
     await mergeKnowledgeGraph({ topics: allExtractedTopics, relations: allExtractedRelations });
     await db.updateSourceStatus(sourceId, 'completed');
 
     return { success: true, data: { topics: allExtractedTopics, relations: allExtractedRelations } };
 
   } catch (error) {
-    await db.updateSourceStatus(sourceId, 'failed', error.message);
+    const isAbort = error?.name === 'AbortError';
+    await db.updateSourceStatus(sourceId, isAbort ? 'cancelled' : 'failed', isAbort ? 'cancelled by user' : error.message);
     throw error;
   }
 }
@@ -169,14 +225,17 @@ async function mergeKnowledgeGraph(newData) {
   if (newData.topics && Array.isArray(newData.topics)) {
     for (const t of newData.topics) {
       if (topicsMap.has(t.id)) {
-        // Upsert: merge new data into existing topic
         const existing = topicsMap.get(t.id);
-        topicsMap.set(t.id, { 
+        const merged = {
           ...existing,
           ...t,
-          // Keep existing description if new one is empty, or combine them if needed. Here we prefer the new one.
-          description: t.description || existing.description 
-        });
+          description: t.description || existing.description,
+        };
+        if (existing.relevance_locked) {
+          merged.learning_relevance = existing.learning_relevance;
+          merged.relevance_locked = true;
+        }
+        topicsMap.set(t.id, merged);
       } else {
         topicsMap.set(t.id, t);
       }

src/lib/learningService.js

@@ -1,51 +1,37 @@
-import { anthropicApi } from './api';
 import * as db from './db';
+import { callLLM } from './llm';
+import {
+  EMIT_LEARNING_ARTICLE_TOOL,
+  EMIT_LEARNING_SLIDES_TOOL,
+  EMIT_LEARNING_INFOGRAPHIC_TOOL,
+  EMIT_LEARNING_ALL_TOOL,
+  EMIT_CUSTOM_TOPIC_TOOL,
+  ARTICLE_PATCH_TOOLS,
+} from './llmTools';
+import { applyAndValidate } from './articlePatches';
 import { getCurriculumTopic } from './curriculumService';
 
 const CONTENT_GENERATION_SYSTEM = `You are an expert learning content writer for Respellion, an internal IT company.
 You write training material for employees based on knowledge topics.
 Always write in clear, professional English.
-ALWAYS return valid JSON only — no markdown code blocks, no extra text.`;
 
-const CONTENT_SCHEMA_ARTICLE = `{
-  "article": {
-    "title": "Article title",
-    "intro": "Short intro of 1-2 sentences",
-    "sections": [
-      { "heading": "Section title", "body": "Section text of at least 3 sentences." }
-    ],
-    "keyTakeaways": ["Takeaway 1", "Takeaway 2", "Takeaway 3"]
-  }
-}`;
+Emit the requested content through the matching tool — do not return prose JSON.`;
 
-const CONTENT_SCHEMA_SLIDES = `{
-  "slides": [
-    { "title": "Slide title", "bullets": ["Point 1", "Point 2", "Point 3"], "speakerNote": "Speaker note for this slide." }
-  ]
-}`;
+const cachedSystem = (text) => [{ type: 'text', text, cache_control: { type: 'ephemeral' } }];
 
+const TOOL_BY_TYPE = {
+  article: EMIT_LEARNING_ARTICLE_TOOL,
+  slides: EMIT_LEARNING_SLIDES_TOOL,
+  infographic: EMIT_LEARNING_INFOGRAPHIC_TOOL,
+  all: EMIT_LEARNING_ALL_TOOL,
+};
 
-
-const CONTENT_SCHEMA_INFOGRAPHIC = `{
-  "infographic": {
-    "headline": "A short, punchy headline summarizing the topic (max 8 words)",
-    "tagline": "A subtitle of max 15 words",
-    "stats": [
-      { "value": "Number or %", "label": "Short description", "icon": "📊" }
-    ],
-    "steps": [
-      { "number": 1, "title": "Step title", "description": "One-sentence description.", "icon": "🔑" }
-    ],
-    "quote": "An inspiring or insightful quote about the topic.",
-    "colorTheme": "teal"
-  }
-}`;
-
-const CONTENT_SCHEMA_ALL = `{
-  "article": ${CONTENT_SCHEMA_ARTICLE.replace(/^\{|\}$/g, '').trim()},
-  "slides": ${CONTENT_SCHEMA_SLIDES.replace(/^\{|\}$/g, '').trim()},
-  "infographic": ${CONTENT_SCHEMA_INFOGRAPHIC.replace(/^\{|\}$/g, '').trim()}
-}`;
+const INSTRUCTIONS_BY_TYPE = {
+  article: 'Provide at least 3 article sections and at least 2 key takeaways.',
+  slides: 'Provide at least 4 slides.',
+  infographic: 'Provide at least 3 stats and 3 steps.',
+  all: 'Provide at least 3 article sections, 4 slides, 3 stats, and 3 steps in the infographic.',
+};
 
 /**
  * Get the assigned topic for a given week.
@@ -53,7 +39,6 @@ const CONTENT_SCHEMA_ALL = `{
  * Falls back to hash-based assignment if no curriculum is configured.
  */
 export async function getAssignedTopic(userId, weekNumber) {
-  // Try curriculum first
   try {
     const { topic } = await getCurriculumTopic(weekNumber);
     if (topic && topic.learning_relevance !== 'exclude') return topic;
@@ -61,9 +46,7 @@ export async function getAssignedTopic(userId, weekNumber) {
     console.warn('[Learn] Curriculum lookup failed, falling back to hash:', e.message);
   }
 
-  // Fallback: hash-based assignment (backwards compatible)
   const allTopics = await db.getTopics();
-  // Filter out 'fact' type topics and 'exclude' relevance topics
   const topics = allTopics.filter(t => t.type !== 'fact' && t.learning_relevance !== 'exclude');
   if (!topics || topics.length === 0) return null;
 
@@ -96,29 +79,15 @@ export async function generateLearningContent(topic, force = false, selectedType
   let cached = null;
   if (!force) {
     cached = await db.getContent(topic.id);
-    if (cached) {
-      if (cached[selectedType]) {
+    if (cached && cached[selectedType]) {
       console.log(`[Learn] Cache hit for topic: ${topic.id} (${selectedType})`);
       return cached;
     }
   }
-  }
 
-  let schema = '';
-  let instructions = '';
-  if (selectedType === 'all') {
-    schema = CONTENT_SCHEMA_ALL;
-    instructions = 'Provide at least 3 article sections, 4 slides, 3 stats, and 3-5 steps in the infographic.';
-  } else if (selectedType === 'article') {
-    schema = CONTENT_SCHEMA_ARTICLE;
-    instructions = 'Provide at least 3 article sections.';
-  } else if (selectedType === 'slides') {
-    schema = CONTENT_SCHEMA_SLIDES;
-    instructions = 'Provide at least 4 slides.';
-  } else if (selectedType === 'infographic') {
-    schema = CONTENT_SCHEMA_INFOGRAPHIC;
-    instructions = 'Provide at least 3 stats, and 3-5 steps in the infographic.';
-  }
+  const tool = TOOL_BY_TYPE[selectedType];
+  if (!tool) throw new Error(`Unknown learning content type: ${selectedType}`);
+  const instructions = INSTRUCTIONS_BY_TYPE[selectedType];
 
   const prompt = `Generate a learning module piece for the following topic:
 
@@ -126,20 +95,20 @@ Label: ${topic.label}
 Type: ${topic.type}
 Description: ${topic.description}
 
-Return ONLY a JSON object with the following structure:
-${schema}
-
 ${instructions}`;
 
-  const responseText = await anthropicApi.generateContent(CONTENT_GENERATION_SYSTEM, prompt);
+  const result = await callLLM({
+    task: `learning.${selectedType}`,
+    tier: 'standard',
+    system: cachedSystem(CONTENT_GENERATION_SYSTEM),
+    user: prompt,
+    tools: [tool],
+    toolChoice: { type: 'tool', name: tool.name },
+    maxTokens: 8192,
+  });
 
-  let newContent;
-  try {
-    const jsonMatch = responseText.match(/\{[\s\S]*\}/);
-    newContent = JSON.parse(jsonMatch ? jsonMatch[0] : responseText);
-  } catch (e) {
-    throw new Error('AI could not generate valid learning content. Please try again.', { cause: e });
-  }
+  const newContent = result.toolUses[0]?.input;
+  if (!newContent) throw new Error('AI did not return learning content. Please try again.');
 
   const mergedContent = { ...(cached || {}), ...newContent };
   await db.setContent(topic.id, mergedContent);
@@ -148,28 +117,37 @@ ${instructions}`;
 
 export async function refineLearningContent(topic, refinementInstruction) {
   const existing = await db.getContent(topic.id);
+  if (!existing?.article) {
+    throw new Error('Refinement is currently only supported for the article. Generate an article for this topic first.');
+  }
 
-  const prompt = `You have previously generated the following learning module for the topic "${topic.label}":
+  const prompt = `You have previously generated the following article for the topic "${topic.label}":
 
-${JSON.stringify(existing, null, 2)}
+${JSON.stringify(existing.article, null, 2)}
 
 The admin has requested the following refinement:
 "${refinementInstruction}"
 
-Apply the refinement and return the complete updated JSON object using the same structure. Return ONLY valid JSON.`;
+Apply the refinement by calling one or more of the available patch tools. Make the smallest set of changes that satisfies the instruction — do not rewrite untouched sections.`;
 
-  const responseText = await anthropicApi.generateContent(CONTENT_GENERATION_SYSTEM, prompt);
+  const result = await callLLM({
+    task: 'learning.refine',
+    tier: 'standard',
+    system: cachedSystem(CONTENT_GENERATION_SYSTEM),
+    user: prompt,
+    tools: ARTICLE_PATCH_TOOLS,
+    toolChoice: { type: 'any' },
+    maxTokens: 4096,
+  });
 
-  let content;
-  try {
-    const jsonMatch = responseText.match(/\{[\s\S]*\}/);
-    content = JSON.parse(jsonMatch ? jsonMatch[0] : responseText);
-  } catch (e) {
-    throw new Error('AI could not process the refinement. Please try a different instruction.', { cause: e });
+  if (!result.toolUses.length) {
+    throw new Error('AI did not propose any changes for that instruction. Try a more specific request.');
   }
 
-  await db.setContent(topic.id, content);
-  return content;
+  const patchedArticle = applyAndValidate(existing.article, result.toolUses);
+  const merged = { ...existing, article: patchedArticle };
+  await db.setContent(topic.id, merged);
+  return merged;
 }
 
 export async function deleteCachedContent(topicId) {
@@ -177,30 +155,20 @@ export async function deleteCachedContent(topicId) {
 }
 
 export async function generateCustomTopic(label) {
-  const prompt = `A user wants to learn about "${label}".
-Create a short description (2-3 sentences) and categorize it.
+  const result = await callLLM({
+    task: 'topic.custom',
+    tier: 'standard',
+    system: cachedSystem('You are a knowledge graph AI categorising user-requested topics for the Respellion learning platform.'),
+    user: `A user wants to learn about "${label}". Provide a polished label, type, and 2–3 sentence description via the emit_custom_topic tool.`,
+    tools: [EMIT_CUSTOM_TOPIC_TOOL],
+    toolChoice: { type: 'tool', name: EMIT_CUSTOM_TOPIC_TOOL.name },
+    maxTokens: 1024,
+  });
 
-Return ONLY a JSON object with this structure:
-{
-  "label": "Polished topic title",
-  "type": "concept", // one of: concept, role, process
-  "description": "Short description"
-}`;
-
-  const responseText = await anthropicApi.generateContent(
-    "You are a knowledge graph AI categorizing topics.",
-    prompt
-  );
-
-  let newTopic;
-  try {
-    const jsonMatch = responseText.match(/\{[\s\S]*\}/);
-    newTopic = JSON.parse(jsonMatch ? jsonMatch[0] : responseText);
-    newTopic.id = 'custom_' + Date.now().toString(36);
-  } catch (e) {
-    throw new Error('Could not process custom topic. Please try again.', { cause: e });
-  }
+  const emitted = result.toolUses[0]?.input;
+  if (!emitted) throw new Error('Could not process custom topic. Please try again.');
 
+  const newTopic = { ...emitted, id: 'custom_' + Date.now().toString(36) };
   await db.upsertTopic(newTopic);
   return newTopic;
 }

src/lib/llm.js

@@ -125,7 +125,7 @@ function isChatLikeTask(task) {
   return task === 'legacy.chat' || task.startsWith('chat.') || task.startsWith('r42.');
 }
 
-const SIMULATION_EXTRACTION_PAYLOAD = JSON.stringify({
+const SIMULATION_EXTRACTION_GRAPH = {
   topics: [
     { id: 'radicale-transparantie', label: 'Radicale Transparantie', type: 'concept', description: 'De kernwaarde van Respellion waarbij alle informatie publiek toegankelijk is.', learning_relevance: 'core' },
     { id: 'kennisbeheer', label: 'Kennisbeheer', type: 'process', description: 'Het proces van het vastleggen en ontsluiten van organisatiekennis.', learning_relevance: 'standard' },
@@ -135,33 +135,87 @@ const SIMULATION_EXTRACTION_PAYLOAD = JSON.stringify({
     { source: 'kennisbeheer', target: 'radicale-transparantie', type: 'depends_on' },
     { source: 'wekelijkse-sessie', target: 'kennisbeheer', type: 'part_of' },
   ],
-});
+};
+
+const SIMULATION_EXTRACTION_PAYLOAD = JSON.stringify(SIMULATION_EXTRACTION_GRAPH);
 
 const SIMULATION_CHAT_TEXT =
   'Simulatiemodus staat aan — vraag een beheerder om Simulation Mode uit te zetten in Admin → Settings om met R42 te chatten.';
 
-async function simulatedResponse({ task }) {
+const SIMULATION_ARTICLE = {
+  title: 'Voorbeeld leermodule',
+  intro: 'Dit is een simulatie. Schakel Simulation Mode uit om echte content te genereren.',
+  sections: [
+    { heading: 'Wat dit is', body: 'Dit is een placeholder-sectie die alleen verschijnt wanneer simulatiemodus aan staat. Hij illustreert de structuur van het artikel zonder een echte API-aanroep te doen. Dat is handig voor UI-werk.' },
+  ],
+  keyTakeaways: ['Simulatiemodus levert geen echte inhoud.', 'Schakel uit voor productie.'],
+};
+
+const SIMULATION_SLIDE = {
+  title: 'Voorbeeldslide',
+  bullets: ['Eerste punt', 'Tweede punt'],
+  speakerNote: 'Spreker-notitie ter illustratie.',
+};
+
+const SIMULATION_INFOGRAPHIC = {
+  headline: 'Simulatie',
+  tagline: 'Vervang door echte content',
+  stats: [{ value: '100%', label: 'simulatie', icon: '📊' }],
+  steps: [{ number: 1, title: 'Schakel uit', description: 'Zet simulatiemodus uit in Admin → Settings.', icon: '🔧' }],
+  quote: 'Een simulatie vertelt niets nieuws.',
+  colorTheme: 'teal',
+};
+
+const SIMULATION_TOOL_STUBS = {
+  emit_knowledge_graph: SIMULATION_EXTRACTION_GRAPH,
+  emit_handbook_delta: SIMULATION_EXTRACTION_GRAPH,
+  emit_learning_article: { article: SIMULATION_ARTICLE },
+  emit_learning_slides: { slides: [SIMULATION_SLIDE] },
+  emit_learning_infographic: { infographic: SIMULATION_INFOGRAPHIC },
+  emit_learning_all: { article: SIMULATION_ARTICLE, slides: [SIMULATION_SLIDE], infographic: SIMULATION_INFOGRAPHIC },
+  emit_custom_topic: { label: 'Simulatie onderwerp', type: 'concept', description: 'Een placeholder-onderwerp gegenereerd in simulatiemodus.' },
+  emit_quiz_questions: {
+    questions: [
+      {
+        id: 'sim-q1',
+        question: 'Wat doet simulatiemodus?',
+        topicLabel: 'Simulatie',
+        options: ['Echte API-aanroepen', 'Stub-data tonen', 'Niets', 'Crasht de app'],
+        correctIndex: 1,
+        explanation: 'Simulatiemodus retourneert vaste stub-data zonder de API te raken.',
+      },
+    ],
+  },
+  emit_graph_actions: { merges: [], deletions: [], newRelations: [], relevanceUpdates: [] },
+  set_intro: { intro: 'Bijgewerkte intro (simulatie).' },
+};
+
+function stubResponse({ stopReason = 'end_turn', text = '', toolUses = [] }) {
+  return {
+    text,
+    toolUses,
+    stopReason,
+    usage: { input_tokens: 0, output_tokens: 0, cache_creation_input_tokens: 0, cache_read_input_tokens: 0 },
+    requestId: null,
+    model: 'simulation',
+    durationMs: 400,
+  };
+}
+
+async function simulatedResponse({ task, toolChoice }) {
   await new Promise((r) => setTimeout(r, 400));
-  if (isChatLikeTask(task)) {
-    return {
-      text: SIMULATION_CHAT_TEXT,
-      toolUses: [],
-      stopReason: 'end_turn',
-      usage: { input_tokens: 0, output_tokens: 0, cache_creation_input_tokens: 0, cache_read_input_tokens: 0 },
-      requestId: null,
-      model: 'simulation',
-      durationMs: 400,
-    };
+
+  if (toolChoice?.type === 'tool' && SIMULATION_TOOL_STUBS[toolChoice.name]) {
+    return stubResponse({
+      stopReason: 'tool_use',
+      toolUses: [{ name: toolChoice.name, input: SIMULATION_TOOL_STUBS[toolChoice.name] }],
+    });
   }
-  return {
-    text: SIMULATION_EXTRACTION_PAYLOAD,
-    toolUses: [],
-    stopReason: 'end_turn',
-    usage: { input_tokens: 0, output_tokens: 0, cache_creation_input_tokens: 0, cache_read_input_tokens: 0 },
-    requestId: null,
-    model: 'simulation',
-    durationMs: 400,
-  };
+
+  if (isChatLikeTask(task)) {
+    return stubResponse({ text: SIMULATION_CHAT_TEXT });
+  }
+  return stubResponse({ text: SIMULATION_EXTRACTION_PAYLOAD });
 }
 
 function linkSignals(userSignal, timeoutSignal) {
@@ -218,6 +272,7 @@ function validateToolInputs(toolUses, task, toolSchemas) {
  * @property {number} [maxTokens=4096]
  * @property {number} [temperature=0]
  * @property {AbortSignal} [signal]
+ * @property {{ acquire: (opts?:{signal?:AbortSignal}) => Promise<void>, pauseUntil: (untilMs:number) => void }} [limiter]
  */
 
 /**
@@ -237,11 +292,12 @@ export async function callLLM(options) {
     maxTokens = 4096,
     temperature = 0,
     signal,
+    limiter,
   } = options;
   if (!task) throw new Error('callLLM requires a `task` label.');
 
   const useSimulation = storage.get('admin:use_simulation') === true;
-  if (useSimulation) return simulatedResponse({ task });
+  if (useSimulation) return simulatedResponse({ task, toolChoice });
 
   const model = resolveModel(tier);
   const messagesPayload = buildMessages({ messages, user });
@@ -249,9 +305,12 @@ export async function callLLM(options) {
   const body = {
     model,
     max_tokens: maxTokens,
-    temperature,
     messages: messagesPayload,
   };
+  // Temperature is not supported for reasoning tier models
+  if (tier !== 'reasoning') {
+    body.temperature = temperature;
+  }
   if (system !== undefined) body.system = system;
   if (tools && tools.length) body.tools = tools;
   if (toolChoice) body.tool_choice = toolChoice;
@@ -261,6 +320,7 @@ export async function callLLM(options) {
   try {
     result = await withRetry(
       async () => {
+        if (limiter) await limiter.acquire({ signal });
         const timeoutCtl = signal ? null : new AbortController();
         const timer = timeoutCtl ? setTimeout(() => timeoutCtl.abort(new DOMException('Timeout', 'AbortError')), DEFAULT_TIMEOUT_MS) : null;
         const fetchSignal = linkSignals(signal, timeoutCtl?.signal);
@@ -280,6 +340,9 @@ export async function callLLM(options) {
             const errBody = await response.json().catch(() => ({}));
             if (isRetryableStatus(response.status)) {
               const retryAfterMs = parseRetryAfter(response.headers.get('Retry-After'));
+              if (response.status === 429 && retryAfterMs != null && limiter) {
+                limiter.pauseUntil(Date.now() + retryAfterMs);
+              }
               throw new RetryableError(response.status, retryAfterMs, `HTTP ${response.status}`);
             }
             throw new LLMHttpError(response.status, response.statusText, errBody);

src/lib/llmRetry.js

@@ -62,6 +62,109 @@ function sleep(ms, signal) {
   });
 }
 
+/**
+ * Token-bucket rate limiter shared by callers that hit the same upstream
+ * quota (e.g. handbook extraction loops). Replaces the static
+ * `setTimeout(..., 12000)` / 15s sleeps that Phase 1 relied on. The bucket
+ * refills continuously; `acquire` resolves either immediately (token
+ * available) or after the next refill tick.
+ *
+ * `rps` is "requests per second" (use fractional values for per-minute
+ * limits: `5/60` for 5 req/min). `burst` is the maximum number of tokens
+ * the bucket can hold; default 1 means strict spacing.
+ *
+ * Call `pauseUntil(timestampMs)` after a 429 with a `Retry-After` hint —
+ * no acquire returns before that timestamp.
+ *
+ * @param {{ rps?: number, burst?: number }} [opts]
+ */
+export function createLimiter({ rps = 1, burst = 1 } = {}) {
+  if (rps <= 0) throw new Error('createLimiter: rps must be > 0');
+  if (burst < 1) throw new Error('createLimiter: burst must be >= 1');
+  const intervalMs = 1000 / rps;
+  let tokens = burst;
+  let lastRefill = Date.now();
+  let pausedUntil = 0;
+  const waiters = [];
+
+  function refill(now) {
+    const elapsed = now - lastRefill;
+    if (elapsed <= 0) return;
+    const earned = elapsed / intervalMs;
+    if (earned >= 1) {
+      tokens = Math.min(burst, tokens + Math.floor(earned));
+      lastRefill = now;
+    }
+  }
+
+  function drain() {
+    while (waiters.length) {
+      const now = Date.now();
+      if (now < pausedUntil) {
+        scheduleWake(pausedUntil - now);
+        return;
+      }
+      refill(now);
+      if (tokens >= 1) {
+        tokens -= 1;
+        const w = waiters.shift();
+        w.resolve();
+      } else {
+        const wait = Math.max(intervalMs - (now - lastRefill), 0);
+        scheduleWake(wait);
+        return;
+      }
+    }
+  }
+
+  let wakeTimer = null;
+  function scheduleWake(ms) {
+    if (wakeTimer) return;
+    wakeTimer = setTimeout(() => {
+      wakeTimer = null;
+      drain();
+    }, ms);
+  }
+
+  return {
+    /** @param {{signal?:AbortSignal}} [opts] */
+    async acquire({ signal } = {}) {
+      if (signal?.aborted) throw signal.reason ?? new DOMException('Aborted', 'AbortError');
+      return new Promise((resolve, reject) => {
+        const entry = { resolve, reject };
+        const onAbort = () => {
+          const i = waiters.indexOf(entry);
+          if (i !== -1) waiters.splice(i, 1);
+          reject(signal.reason ?? new DOMException('Aborted', 'AbortError'));
+        };
+        if (signal) signal.addEventListener('abort', onAbort, { once: true });
+        const wrapped = {
+          resolve: () => { if (signal) signal.removeEventListener('abort', onAbort); resolve(); },
+          reject,
+        };
+        waiters.push(wrapped);
+        drain();
+      });
+    },
+    /** Block all `acquire`s until `untilMs` (epoch milliseconds). */
+    pauseUntil(untilMs) {
+      if (untilMs > pausedUntil) pausedUntil = untilMs;
+      drain();
+    },
+    /** Inspect state — primarily for tests. */
+    _state() {
+      return { tokens, pausedUntil, waiters: waiters.length };
+    },
+  };
+}
+
+/**
+ * Shared limiter for the multi-call extraction loops (source chunks,
+ * handbook file sync). 5 requests/minute matches the lowest published
+ * Anthropic tier so we stay well clear of 429.
+ */
+export const extractionLimiter = createLimiter({ rps: 5 / 60, burst: 1 });
+
 /**
  * Run `fn(attempt)` with retry. `fn` may throw a `RetryableError` to request
  * a retry, or any other error to fail immediately.

src/lib/llmSchemas.js

@@ -183,6 +183,31 @@ export const proposeGraphDeltaSchema = z.object({
   relations: z.array(deltaRelationSchema).max(5).optional(),
 });
 
+// ── Article patch operation schemas (Phase 2.4) ──────────────────────────────
+
+export const setIntroPatchSchema = z.object({
+  intro: z.string().min(1),
+});
+
+export const setSectionPatchSchema = z.object({
+  heading: z.string().min(1),
+  body: z.string().min(1),
+});
+
+export const addSectionPatchSchema = z.object({
+  heading: z.string().min(1),
+  body: z.string().min(1),
+  position: z.enum(['start', 'end']),
+});
+
+export const removeSectionPatchSchema = z.object({
+  heading: z.string().min(1),
+});
+
+export const replaceTakeawaysPatchSchema = z.object({
+  items: z.array(z.string().min(1)).min(1),
+});
+
 /**
  * Registry mapping known tool names to their input schemas. `callLLM`
  * consults this when the caller does not pass an explicit `toolSchemas`
@@ -199,4 +224,9 @@ export const toolSchemaRegistry = {
   emit_custom_topic: customTopicSchema,
   emit_graph_actions: graphActionsSchema,
   propose_graph_delta: proposeGraphDeltaSchema,
+  set_intro: setIntroPatchSchema,
+  set_section: setSectionPatchSchema,
+  add_section: addSectionPatchSchema,
+  remove_section: removeSectionPatchSchema,
+  replace_takeaways: replaceTakeawaysPatchSchema,
 };

src/lib/llmTools.js

@@ -0,0 +1,324 @@
+/**
+ * Anthropic tool definitions used by every structured-output flow.
+ *
+ * Each `tool_use` reply the model emits is validated against the matching
+ * Zod schema in `llmSchemas.js` (see `toolSchemaRegistry`). The two stay
+ * in lock-step on purpose — JSON Schema here drives the model, Zod there
+ * defends the application.
+ */
+
+const TOPIC_TYPES = ['concept', 'role', 'process'];
+const LEARNING_RELEVANCE = ['core', 'standard', 'peripheral', 'exclude'];
+const RELATION_TYPES_STRICT = ['related_to', 'depends_on', 'part_of', 'executed_by'];
+const RELATION_TYPES_LOOSE = ['related_to', 'depends_on', 'part_of', 'executed_by', 'executes'];
+
+const extractionTopicSchema = {
+  type: 'object',
+  properties: {
+    id: { type: 'string', description: 'kebab-case slug specific to the topic. Reuse existing IDs when the same concept recurs.' },
+    label: { type: 'string' },
+    type: { type: 'string', enum: TOPIC_TYPES },
+    description: { type: 'string', description: 'Max 3 sentences.' },
+    learning_relevance: { type: 'string', enum: LEARNING_RELEVANCE },
+  },
+  required: ['id', 'label', 'type', 'description', 'learning_relevance'],
+};
+
+const extractionRelationSchema = {
+  type: 'object',
+  properties: {
+    source: { type: 'string', description: 'Topic id.' },
+    target: { type: 'string', description: 'Topic id.' },
+    type: { type: 'string', enum: RELATION_TYPES_STRICT },
+  },
+  required: ['source', 'target', 'type'],
+};
+
+export const EMIT_KNOWLEDGE_GRAPH_TOOL = {
+  name: 'emit_knowledge_graph',
+  description: 'Return the complete knowledge graph extracted from the supplied source text — every distinct role, process and concept as a topic, plus the relations between them.',
+  input_schema: {
+    type: 'object',
+    properties: {
+      topics: { type: 'array', items: extractionTopicSchema },
+      relations: { type: 'array', items: extractionRelationSchema },
+    },
+    required: ['topics', 'relations'],
+  },
+};
+
+const handbookTopicSchema = {
+  type: 'object',
+  properties: {
+    ...extractionTopicSchema.properties,
+    metadata: {
+      type: 'object',
+      properties: { source: { type: 'string' } },
+    },
+  },
+  required: extractionTopicSchema.required,
+};
+
+const handbookRelationSchema = {
+  type: 'object',
+  properties: {
+    source: { type: 'string' },
+    target: { type: 'string' },
+    type: { type: 'string', enum: RELATION_TYPES_LOOSE },
+    description: { type: 'string' },
+  },
+  required: ['source', 'target', 'type'],
+};
+
+export const EMIT_HANDBOOK_DELTA_TOOL = {
+  name: 'emit_handbook_delta',
+  description: 'Return the topics and relations extracted from a handbook file update. Every process must have a role attached; every concept must connect to a process or role.',
+  input_schema: {
+    type: 'object',
+    properties: {
+      topics: { type: 'array', items: handbookTopicSchema },
+      relations: { type: 'array', items: handbookRelationSchema },
+    },
+    required: ['topics', 'relations'],
+  },
+};
+
+const articleSectionSchema = {
+  type: 'object',
+  properties: {
+    heading: { type: 'string' },
+    body: { type: 'string', description: 'At least three sentences.' },
+  },
+  required: ['heading', 'body'],
+};
+
+const articleBodySchema = {
+  type: 'object',
+  properties: {
+    title: { type: 'string' },
+    intro: { type: 'string', description: 'One or two sentences.' },
+    sections: { type: 'array', items: articleSectionSchema, minItems: 1 },
+    keyTakeaways: { type: 'array', items: { type: 'string' }, minItems: 1 },
+  },
+  required: ['title', 'intro', 'sections', 'keyTakeaways'],
+};
+
+const slideSchema = {
+  type: 'object',
+  properties: {
+    title: { type: 'string' },
+    bullets: { type: 'array', items: { type: 'string' }, minItems: 1 },
+    speakerNote: { type: 'string' },
+  },
+  required: ['title', 'bullets', 'speakerNote'],
+};
+
+const infographicStatSchema = {
+  type: 'object',
+  properties: {
+    value: { type: 'string' },
+    label: { type: 'string' },
+    icon: { type: 'string' },
+  },
+  required: ['value', 'label', 'icon'],
+};
+
+const infographicStepSchema = {
+  type: 'object',
+  properties: {
+    number: { type: 'integer', minimum: 1 },
+    title: { type: 'string' },
+    description: { type: 'string' },
+    icon: { type: 'string' },
+  },
+  required: ['number', 'title', 'description', 'icon'],
+};
+
+const infographicBodySchema = {
+  type: 'object',
+  properties: {
+    headline: { type: 'string', description: 'Punchy, max 8 words.' },
+    tagline: { type: 'string', description: 'Max 15 words.' },
+    stats: { type: 'array', items: infographicStatSchema, minItems: 1 },
+    steps: { type: 'array', items: infographicStepSchema, minItems: 1 },
+    quote: { type: 'string' },
+    colorTheme: { type: 'string', description: 'Tailwind colour token (e.g. "teal").' },
+  },
+  required: ['headline', 'tagline', 'stats', 'steps', 'quote', 'colorTheme'],
+};
+
+export const EMIT_LEARNING_ARTICLE_TOOL = {
+  name: 'emit_learning_article',
+  description: 'Return the article body for a learning module. At least three sections.',
+  input_schema: {
+    type: 'object',
+    properties: { article: articleBodySchema },
+    required: ['article'],
+  },
+};
+
+export const EMIT_LEARNING_SLIDES_TOOL = {
+  name: 'emit_learning_slides',
+  description: 'Return the slide deck for a learning module. At least four slides.',
+  input_schema: {
+    type: 'object',
+    properties: { slides: { type: 'array', items: slideSchema, minItems: 1 } },
+    required: ['slides'],
+  },
+};
+
+export const EMIT_LEARNING_INFOGRAPHIC_TOOL = {
+  name: 'emit_learning_infographic',
+  description: 'Return the infographic for a learning module. At least three stats and three steps.',
+  input_schema: {
+    type: 'object',
+    properties: { infographic: infographicBodySchema },
+    required: ['infographic'],
+  },
+};
+
+export const EMIT_LEARNING_ALL_TOOL = {
+  name: 'emit_learning_all',
+  description: 'Return article, slides and infographic for a learning module in one call.',
+  input_schema: {
+    type: 'object',
+    properties: {
+      article: articleBodySchema,
+      slides: { type: 'array', items: slideSchema, minItems: 1 },
+      infographic: infographicBodySchema,
+    },
+    required: ['article', 'slides', 'infographic'],
+  },
+};
+
+export const EMIT_CUSTOM_TOPIC_TOOL = {
+  name: 'emit_custom_topic',
+  description: 'Return a polished label, type and short description for a user-requested topic.',
+  input_schema: {
+    type: 'object',
+    properties: {
+      label: { type: 'string' },
+      type: { type: 'string', enum: TOPIC_TYPES },
+      description: { type: 'string', description: 'Two or three sentences.' },
+    },
+    required: ['label', 'type', 'description'],
+  },
+};
+
+const quizQuestionSchema = {
+  type: 'object',
+  properties: {
+    id: { type: 'string' },
+    question: { type: 'string' },
+    topicLabel: { type: 'string' },
+    options: { type: 'array', items: { type: 'string' }, minItems: 4, maxItems: 4 },
+    correctIndex: { type: 'integer', minimum: 0, maximum: 3 },
+    explanation: { type: 'string', description: 'Why the correct answer is correct (1–2 sentences).' },
+  },
+  required: ['id', 'question', 'topicLabel', 'options', 'correctIndex', 'explanation'],
+};
+
+export const EMIT_QUIZ_QUESTIONS_TOOL = {
+  name: 'emit_quiz_questions',
+  description: 'Return a batch of multiple-choice questions for a topic. Exactly four options each; correctIndex is 0-based.',
+  input_schema: {
+    type: 'object',
+    properties: { questions: { type: 'array', items: quizQuestionSchema, minItems: 1 } },
+    required: ['questions'],
+  },
+};
+
+export const EMIT_GRAPH_ACTIONS_TOOL = {
+  name: 'emit_graph_actions',
+  description: 'Return the actions to take on the knowledge graph: merges, deletions, new relations and relevance updates. Do not return the entire graph.',
+  input_schema: {
+    type: 'object',
+    properties: {
+      merges: {
+        type: 'array',
+        items: {
+          type: 'object',
+          properties: { keepId: { type: 'string' }, deleteId: { type: 'string' } },
+          required: ['keepId', 'deleteId'],
+        },
+      },
+      deletions: { type: 'array', items: { type: 'string' } },
+      newRelations: { type: 'array', items: extractionRelationSchema },
+      relevanceUpdates: {
+        type: 'array',
+        items: {
+          type: 'object',
+          properties: { id: { type: 'string' }, learning_relevance: { type: 'string', enum: LEARNING_RELEVANCE } },
+          required: ['id', 'learning_relevance'],
+        },
+      },
+    },
+  },
+};
+
+// ── Patch tools for refineLearningContent (Phase 2.4) ─────────────────────────
+
+export const SET_INTRO_TOOL = {
+  name: 'set_intro',
+  description: 'Replace the article intro with a new one or two sentences.',
+  input_schema: {
+    type: 'object',
+    properties: { intro: { type: 'string', description: 'New intro text.' } },
+    required: ['intro'],
+  },
+};
+
+export const SET_SECTION_TOOL = {
+  name: 'set_section',
+  description: 'Replace the body of an existing section, matched by its heading (case-insensitive). Use add_section if no section with that heading exists.',
+  input_schema: {
+    type: 'object',
+    properties: {
+      heading: { type: 'string', description: 'Heading of the section to replace.' },
+      body: { type: 'string', description: 'New body for that section, at least three sentences.' },
+    },
+    required: ['heading', 'body'],
+  },
+};
+
+export const ADD_SECTION_TOOL = {
+  name: 'add_section',
+  description: 'Insert a new section into the article at the start or end.',
+  input_schema: {
+    type: 'object',
+    properties: {
+      heading: { type: 'string' },
+      body: { type: 'string', description: 'At least three sentences.' },
+      position: { type: 'string', enum: ['start', 'end'] },
+    },
+    required: ['heading', 'body', 'position'],
+  },
+};
+
+export const REMOVE_SECTION_TOOL = {
+  name: 'remove_section',
+  description: 'Delete a section from the article, matched by its heading (case-insensitive).',
+  input_schema: {
+    type: 'object',
+    properties: { heading: { type: 'string' } },
+    required: ['heading'],
+  },
+};
+
+export const REPLACE_TAKEAWAYS_TOOL = {
+  name: 'replace_takeaways',
+  description: 'Replace the key takeaways list with a new one.',
+  input_schema: {
+    type: 'object',
+    properties: { items: { type: 'array', items: { type: 'string' }, minItems: 1 } },
+    required: ['items'],
+  },
+};
+
+export const ARTICLE_PATCH_TOOLS = [
+  SET_INTRO_TOOL,
+  SET_SECTION_TOOL,
+  ADD_SECTION_TOOL,
+  REMOVE_SECTION_TOOL,
+  REPLACE_TAKEAWAYS_TOOL,
+];

src/lib/testService.js

@@ -1,11 +1,15 @@
-import { anthropicApi } from './api';
 import * as db from './db';
+import { callLLM } from './llm';
+import { EMIT_QUIZ_QUESTIONS_TOOL } from './llmTools';
 import { getCurriculumTopic, getQuarterForWeek } from './curriculumService';
 
 const QUIZ_SYSTEM = `You are a quiz generator for Respellion, an internal IT company learning platform.
 You generate multiple-choice questions to test employee knowledge on specific topics.
 Always write in clear, professional English.
-ALWAYS return valid JSON only — no markdown code blocks, no extra text.`;
+
+Emit questions through the emit_quiz_questions tool. Each question has exactly four options; correctIndex is 0-based; mix difficulty roughly 4 easy / 4 medium / 2 hard.`;
+
+const cachedSystem = (text) => [{ type: 'text', text, cache_control: { type: 'ephemeral' } }];
 
 async function selectTestTopics(userId, weekNumber) {
   const allTopics = await db.getTopics();
@@ -66,45 +70,31 @@ export async function getCachedQuiz(userId, weekNumber) {
 export async function forceGenerateTopicQuestions(topic, count = 10) {
   let bank = await db.getQuizBank(topic.id);
 
-  const prompt = `Generate exactly ${count} multiple-choice quiz questions based on this knowledge topic:
+  const prompt = `Generate exactly ${count} multiple-choice quiz questions for this knowledge topic and emit them via the emit_quiz_questions tool:
 
 Topic: ${topic.label}
 Type: ${topic.type}
 Description: ${topic.description}
 
-Return ONLY a JSON object with this structure:
-{
-  "questions": [
-    {
-      "id": "unique-id-string",
-      "question": "The question text",
-      "topicLabel": "${topic.label}",
-      "options": ["A) First option", "B) Second option", "C) Third option", "D) Fourth option"],
-      "correctIndex": 0,
-      "explanation": "A clear 1-2 sentence explanation of why the correct answer is correct."
-    }
-  ]
-}
+Options must be prefixed "A) ", "B) ", "C) ", "D) ". Make questions specific and practical, not trivial.`;
 
-Rules:
-- Each question must have exactly 4 options.
-- correctIndex is 0-based (0=A, 1=B, 2=C, 3=D).
-- Mix difficulty: 4 easy, 4 medium, 2 hard.
-- Make questions specific and practical, not trivial.`;
-
-  const responseText = await anthropicApi.generateContent(QUIZ_SYSTEM, prompt);
-  let newQuestions;
-  try {
-    const jsonMatch = responseText.match(/\{[\s\S]*\}/);
-    const parsed = JSON.parse(jsonMatch ? jsonMatch[0] : responseText);
-    newQuestions = parsed.questions || [];
-    newQuestions.forEach(q => {
-      q.id = `${topic.id}-${Math.random().toString(36).substr(2, 9)}`;
+  const result = await callLLM({
+    task: 'quiz.generate',
+    tier: 'standard',
+    system: cachedSystem(QUIZ_SYSTEM),
+    user: prompt,
+    tools: [EMIT_QUIZ_QUESTIONS_TOOL],
+    toolChoice: { type: 'tool', name: EMIT_QUIZ_QUESTIONS_TOOL.name },
+    maxTokens: 4096,
   });
-  } catch (e) {
-    console.error('Failed to generate questions for topic', topic.label, e);
-    throw new Error(`Could not generate questions for ${topic.label}`, { cause: e });
-  }
+
+  const emitted = result.toolUses[0]?.input;
+  if (!emitted) throw new Error(`Could not generate questions for ${topic.label}`);
+
+  const newQuestions = (emitted.questions || []).map(q => ({
+    ...q,
+    id: `${topic.id}-${Math.random().toString(36).slice(2, 11)}`,
+  }));
 
   bank = [...bank, ...newQuestions];
   await db.setQuizBank(topic.id, bank);

vite.config.js

@@ -1,9 +1,24 @@
 import { defineConfig } from 'vite'
+import { execSync } from 'node:child_process'
 import react from '@vitejs/plugin-react'
 import tailwindcss from '@tailwindcss/vite'
 
+function getBuildSha() {
+  try {
+    return execSync('git rev-parse --short HEAD', { stdio: ['ignore', 'pipe', 'ignore'] })
+      .toString()
+      .trim()
+  } catch {
+    return 'unknown'
+  }
+}
+
 // https://vite.dev/config/
 export default defineConfig({
+  define: {
+    __BUILD_SHA__: JSON.stringify(getBuildSha()),
+    __BUILD_TIME__: JSON.stringify(new Date().toISOString()),
+  },
   plugins: [react(), tailwindcss()],
   server: {
     proxy: {