feat: Step 10 - Node Editor & AI-Powered Linking
Implemented the node editor page and AI-powered link suggestions: 1. Node Editor Page (/editor/[id]): - Form with title and body fields using Mantine - Pre-fill support from query params (for chat redirects) - "Find Related" button to discover similar nodes - "Publish Node" button to save to ATproto + SurrealDB - Display of suggested links with similarity scores - Mantine notifications for success/error feedback 2. Suggest Links API (/api/suggest-links): - Authenticates using SurrealDB JWT cookie - Generates embedding for draft text using Google AI - Performs vector similarity search using SurrealDB - Returns top 5 most similar nodes with cosine scores - Enforces row-level security (users only see their nodes) 3. Magnitude Tests: - Editor page rendering - Pre-filled form from query params - Publishing new nodes - Form validation The editor integrates with the existing /api/nodes write-through cache from Step 6, completing the node creation workflow. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
74
app/api/suggest-links/route.ts
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74
app/api/suggest-links/route.ts
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import { NextRequest, NextResponse } from 'next/server';
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import { cookies } from 'next/headers';
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import { connectToDB } from '@/lib/db';
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import { generateEmbedding } from '@/lib/ai';
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/**
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* POST /api/suggest-links
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*
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* Uses vector similarity search to find related nodes.
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* Takes the body text of a draft node, generates an embedding,
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* and returns the top 5 most similar nodes using cosine similarity.
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*/
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export async function POST(request: NextRequest) {
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const cookieStore = await cookies();
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const surrealJwt = cookieStore.get('ponderants-auth')?.value;
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if (!surrealJwt) {
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return NextResponse.json({ error: 'Not authenticated' }, { status: 401 });
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}
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const { body } = (await request.json()) as { body: string };
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if (!body) {
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return NextResponse.json({ error: 'Body text is required' }, { status: 400 });
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}
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try {
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// 1. Generate embedding for the current draft
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const draftEmbedding = await generateEmbedding(body);
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// 2. Connect to DB (as the user)
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// This enforces row-level security - user can only search their own nodes
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const db = await connectToDB(surrealJwt);
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// 3. Run the vector similarity search query
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// This query finds the 5 closest nodes in the 'node' table
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// using cosine similarity on the 'embedding' field.
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// It only searches nodes WHERE user_did = $token.did,
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// which is enforced by the table's PERMISSIONS.
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const query = `
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SELECT
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id,
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title,
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body,
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atp_uri,
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vector::similarity::cosine(embedding, $draft_embedding) AS score
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FROM node
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ORDER BY score DESC
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LIMIT 5;
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`;
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const results = await db.query<[Array<{
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id: string;
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title: string;
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body: string;
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atp_uri: string;
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score: number;
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}>]>(query, {
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draft_embedding: draftEmbedding,
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});
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// The query returns an array of result sets. We want the first one.
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const suggestions = results[0] || [];
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return NextResponse.json(suggestions);
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} catch (error) {
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console.error('[Suggest Links] Error:', error);
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return NextResponse.json(
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{ error: 'Failed to suggest links' },
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{ status: 500 }
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);
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}
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}
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187
app/editor/[id]/page.tsx
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187
app/editor/[id]/page.tsx
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'use client';
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import {
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Container,
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Title,
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TextInput,
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Textarea,
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Button,
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Stack,
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Paper,
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Text,
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LoadingOverlay,
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Group,
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} from '@mantine/core';
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import { useForm } from '@mantine/form';
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import { useSearchParams, useRouter, useParams } from 'next/navigation';
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import { useEffect, useState } from 'react';
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import { notifications } from '@mantine/notifications';
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// Define the shape of a suggested link
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interface SuggestedNode {
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id: string;
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title: string;
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body: string;
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score: number;
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}
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export default function EditorPage() {
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const router = useRouter();
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const params = useParams();
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const searchParams = useSearchParams();
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const [isPublishing, setIsPublishing] = useState(false);
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const [isFinding, setIsFinding] = useState(false);
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const [suggestions, setSuggestions] = useState<SuggestedNode[]>([]);
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const form = useForm({
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initialValues: {
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title: '',
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body: '',
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links: [] as string[], // Array of at-uri strings
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},
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});
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// Pre-fill form from search params (from AI chat redirect)
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useEffect(() => {
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if (params.id === 'new') {
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const title = searchParams.get('title') || '';
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const body = searchParams.get('body') || '';
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form.setValues({ title, body });
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} else {
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// TODO: Load existing node from /api/nodes/[id]
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}
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}, [params.id, searchParams]);
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// Handler for the "Publish" button (calls Commit 06 API)
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const handlePublish = async (values: typeof form.values) => {
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setIsPublishing(true);
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try {
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const response = await fetch('/api/nodes', {
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method: 'POST',
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headers: { 'Content-Type': 'application/json' },
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body: JSON.stringify(values),
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});
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if (!response.ok) {
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const error = await response.json();
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throw new Error(error.error || 'Failed to publish node');
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}
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const newNode = await response.json();
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notifications.show({
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title: 'Success',
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message: 'Node published successfully!',
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color: 'green',
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});
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// On success, go to the chat (galaxy not implemented yet)
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router.push('/chat');
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} catch (error) {
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console.error(error);
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notifications.show({
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title: 'Error',
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message: error instanceof Error ? error.message : 'Failed to publish node',
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color: 'red',
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});
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} finally {
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setIsPublishing(false);
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}
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};
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// Handler for the "Find related" button
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const handleFindRelated = async () => {
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setIsFinding(true);
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setSuggestions([]);
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try {
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const response = await fetch('/api/suggest-links', {
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method: 'POST',
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headers: { 'Content-Type': 'application/json' },
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body: JSON.stringify({ body: form.values.body }),
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});
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if (!response.ok) {
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const error = await response.json();
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throw new Error(error.error || 'Failed to find links');
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}
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const relatedNodes = await response.json();
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setSuggestions(relatedNodes);
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} catch (error) {
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console.error(error);
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notifications.show({
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title: 'Error',
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message: error instanceof Error ? error.message : 'Failed to find related links',
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color: 'red',
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});
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} finally {
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setIsFinding(false);
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}
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};
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return (
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<Container size="md" py="xl">
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<form onSubmit={form.onSubmit(handlePublish)}>
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<Stack gap="md">
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<Title order={2}>
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{params.id === 'new' ? 'Create New Node' : 'Edit Node'}
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</Title>
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<TextInput
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label="Title"
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placeholder="Your node title"
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required
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{...form.getInputProps('title')}
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/>
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<Textarea
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label="Body"
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placeholder="Your node content..."
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required
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minRows={10}
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autosize
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{...form.getInputProps('body')}
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/>
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<Group>
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<Button
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type="button"
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variant="outline"
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onClick={handleFindRelated}
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loading={isFinding}
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>
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Find Related
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</Button>
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<Button type="submit" loading={isPublishing}>
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Publish Node
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</Button>
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</Group>
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{/* Related Links Section */}
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<Stack>
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{isFinding && <LoadingOverlay visible />}
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{suggestions.length > 0 && <Title order={4}>Suggested Links</Title>}
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{suggestions.map((node) => (
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<Paper key={node.id} withBorder p="sm">
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<Text fw={700}>{node.title}</Text>
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<Text size="sm" lineClamp={2}>{node.body}</Text>
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<Text size="xs" c="dimmed">Similarity: {(node.score * 100).toFixed(0)}%</Text>
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</Paper>
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))}
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{!isFinding && suggestions.length === 0 && form.values.body && (
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<Text size="sm" c="dimmed">
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Click "Find Related" to discover similar nodes
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</Text>
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)}
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</Stack>
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</Stack>
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</form>
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</Container>
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);
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}
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