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>

app/api/suggest-links/route.ts

@@ -0,0 +1,74 @@
+import { NextRequest, NextResponse } from 'next/server';
+import { cookies } from 'next/headers';
+import { connectToDB } from '@/lib/db';
+import { generateEmbedding } from '@/lib/ai';
+
+/**
+ * POST /api/suggest-links
+ *
+ * Uses vector similarity search to find related nodes.
+ * Takes the body text of a draft node, generates an embedding,
+ * and returns the top 5 most similar nodes using cosine similarity.
+ */
+export async function POST(request: NextRequest) {
+  const cookieStore = await cookies();
+  const surrealJwt = cookieStore.get('ponderants-auth')?.value;
+
+  if (!surrealJwt) {
+    return NextResponse.json({ error: 'Not authenticated' }, { status: 401 });
+  }
+
+  const { body } = (await request.json()) as { body: string };
+
+  if (!body) {
+    return NextResponse.json({ error: 'Body text is required' }, { status: 400 });
+  }
+
+  try {
+    // 1. Generate embedding for the current draft
+    const draftEmbedding = await generateEmbedding(body);
+
+    // 2. Connect to DB (as the user)
+    // This enforces row-level security - user can only search their own nodes
+    const db = await connectToDB(surrealJwt);
+
+    // 3. Run the vector similarity search query
+    // This query finds the 5 closest nodes in the 'node' table
+    // using cosine similarity on the 'embedding' field.
+    // It only searches nodes WHERE user_did = $token.did,
+    // which is enforced by the table's PERMISSIONS.
+    const query = `
+      SELECT
+        id,
+        title,
+        body,
+        atp_uri,
+        vector::similarity::cosine(embedding, $draft_embedding) AS score
+      FROM node
+      ORDER BY score DESC
+      LIMIT 5;
+    `;
+
+    const results = await db.query<[Array<{
+      id: string;
+      title: string;
+      body: string;
+      atp_uri: string;
+      score: number;
+    }>]>(query, {
+      draft_embedding: draftEmbedding,
+    });
+
+    // The query returns an array of result sets. We want the first one.
+    const suggestions = results[0] || [];
+
+    return NextResponse.json(suggestions);
+
+  } catch (error) {
+    console.error('[Suggest Links] Error:', error);
+    return NextResponse.json(
+      { error: 'Failed to suggest links' },
+      { status: 500 }
+    );
+  }
+}