feat: implement interactive Knowledge Graph visualization with AI-driven content analysis and handbook synchronization tools
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@@ -3,7 +3,8 @@ import * as d3 from 'd3';
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import { Trash2, Edit2, Save, X, RefreshCw, AlertCircle, Plus, Link as LinkIcon } from 'lucide-react';
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import * as db from '../../lib/db';
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import { anthropicApi } from '../../lib/api';
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import { getRepoFolder } from '../../lib/giteaService';
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import { analyzeHandbookDelta } from '../../lib/extractionPipeline';
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import { getRepoFolder, getFileContent } from '../../lib/giteaService';
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import Button from '../ui/Button';
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import SuggestionsQueue from './SuggestionsQueue';
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@@ -21,6 +22,7 @@ const KnowledgeGraph = () => {
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const [isSyncing, setIsSyncing] = useState(false);
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const [syncResult, setSyncResult] = useState(null);
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const [syncError, setSyncError] = useState(null);
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const [syncProgress, setSyncProgress] = useState(null);
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const [topics, setTopics] = useState([]);
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const [relations, setRelations] = useState([]);
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@@ -221,10 +223,32 @@ const KnowledgeGraph = () => {
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});
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setSyncResult({ added, modified, unchanged });
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const filesToProcess = [...added, ...modified];
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if (filesToProcess.length > 0) {
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setSyncProgress(`Processing 0 of ${filesToProcess.length} files...`);
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let count = 0;
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for (const file of filesToProcess) {
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count++;
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setSyncProgress(`Processing ${count} of ${filesToProcess.length}: ${file.name}...`);
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try {
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const rawContent = await getFileContent('respellion', 'employee-handbook', file.path);
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await analyzeHandbookDelta(rawContent, file.path);
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await db.updateHandbookSyncState(file.path, file.sha);
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} catch (err) {
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console.error('Failed to process file:', file.path, err);
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// We continue processing other files even if one fails
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}
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}
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setSyncProgress('Sync Complete!');
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reloadKb();
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}
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} catch (e) {
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setSyncError(e.message || 'Failed to check GitHub for updates.');
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} finally {
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setIsSyncing(false);
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setTimeout(() => setSyncProgress(null), 5000); // Clear progress after 5s
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}
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};
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@@ -358,9 +382,14 @@ Analyze this graph and return ONLY the optimized JSON object with this EXACT str
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<p>Added files: {syncResult.added.length}</p>
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<p>Modified files: {syncResult.modified.length}</p>
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<p>Unchanged: {syncResult.unchanged.length}</p>
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{(syncResult.added.length > 0 || syncResult.modified.length > 0) && (
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<p className="mt-2 italic text-teal">Ready for Phase 2 implementation.</p>
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)}
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</div>
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)}
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{syncProgress && (
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<div className="mt-2 text-xs text-teal p-2 bg-teal/10 border border-teal/20 rounded">
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<p className="font-medium flex items-center gap-2">
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{syncProgress !== 'Sync Complete!' && <RefreshCw size={12} className="animate-spin" />}
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{syncProgress}
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</p>
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</div>
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)}
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</div>
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@@ -24,8 +24,48 @@ ALWAYS return a valid JSON object in the following format:
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}
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]
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}
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}
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Return JSON only. No markdown blocks or other text.`;
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const HANDBOOK_SYSTEM_PROMPT = `You are analyzing an update to the Respellion Employee Handbook.
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Your task is to identify changes and extract structural knowledge.
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CRITICAL INSTRUCTION:
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You must explicitly identify and create relations between Roles, Processes, and Concepts.
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Every Process must have a Role attached (who does it).
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Every Concept must have a relation to a Process or Role.
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Return a JSON object:
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{
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"topics": [
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{ "id": "...", "label": "...", "type": "role | process | concept", "description": "...", "metadata": { "source": "github_handbook" } }
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],
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"relations": [
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{ "source": "role-id", "target": "process-id", "type": "executes | related_to | depends_on | part_of", "description": "Brief metadata about this specific relation" }
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]
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}
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Return JSON only. No markdown blocks or other text.`;
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export async function analyzeHandbookDelta(fileContent, filePath) {
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try {
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const responseText = await anthropicApi.generateContent(HANDBOOK_SYSTEM_PROMPT, `Analyze the following handbook file update (${filePath}):\n\n${fileContent}`);
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let extractedData;
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try {
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const jsonMatch = responseText.match(/\{[\s\S]*\}/);
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const jsonStr = jsonMatch ? jsonMatch[0] : responseText;
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extractedData = JSON.parse(jsonStr);
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} catch (e) {
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console.error('[Pipeline] AI returned non-JSON response for handbook delta:', responseText?.substring(0, 500));
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throw new Error(`AI response was not valid JSON. The model responded with: "${responseText?.substring(0, 120)}..."`);
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}
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await mergeKnowledgeGraph(extractedData);
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return { success: true, data: extractedData };
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} catch (error) {
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throw error;
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}
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}
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export async function processSourceText(textContent, sourceName) {
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// Deduplicate: skip if a source with the same name was already successfully processed
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const existing = await db.getSources();
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@@ -71,7 +111,16 @@ async function mergeKnowledgeGraph(newData) {
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if (newData.topics && Array.isArray(newData.topics)) {
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for (const t of newData.topics) {
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if (!topicsMap.has(t.id)) {
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if (topicsMap.has(t.id)) {
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// Upsert: merge new data into existing topic
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const existing = topicsMap.get(t.id);
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topicsMap.set(t.id, {
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...existing,
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...t,
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// Keep existing description if new one is empty, or combine them if needed. Here we prefer the new one.
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description: t.description || existing.description
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});
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} else {
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topicsMap.set(t.id, t);
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}
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}
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