app/app/api/suggest-links/route.ts

import { NextRequest, NextResponse } from 'next/server';
import { cookies } from 'next/headers';
import { connectToDB } from '@/lib/db';
import { generateEmbedding } from '@/lib/ai';
import { verifySurrealJwt } from '@/lib/auth/jwt';

/**
 * 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 });
  }

  // Verify JWT to get user's DID
  const userSession = verifySurrealJwt(surrealJwt);
  if (!userSession) {
    return NextResponse.json({ error: 'Invalid auth token' }, { status: 401 });
  }

  const { did: userDid } = userSession;

  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 with root credentials
    const db = await connectToDB();

    // 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.
    // We filter by user_did to ensure users only see their own nodes.
    const query = `
      SELECT
        id,
        title,
        body,
        atp_uri,
        vector::similarity::cosine(embedding, $draft_embedding) AS score
      FROM node
      WHERE user_did = $user_did
      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,
      user_did: userDid,
    });

    // 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 }
    );
  }
}