// src/app/api/search/query/route.ts
import { OpenAI } from 'openai'
import { NextResponse } from 'next/server'
import { Index } from '@upstash/vector'
import { config } from 'dotenv'

config()

const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY! })
const index = Index.fromEnv()

export async function POST(req: Request) {
  const { query } = await req.json()

  if (!query) {
    return NextResponse.json({ error: 'Query is required' }, { status: 400 })
  }

  const embeddingResponse = await openai.embeddings.create({
    model: 'text-embedding-3-small',
    input: query,
  })

  const embedding = embeddingResponse.data[0].embedding

  const search = await index.query({
    vector: embedding,
    topK: 5,
    includeMetadata: true,
  })

  const results = search.map((item) => {
    const { slug, type, title } = item.metadata as {
      slug: string
      type: 'articles' | 'projects'
      title: string
    }

    return {
      id: item.id,
      score: item.score,
      title,
      type,
      slug,
      const url = `/${type}/${slug}` // Adjust this to match your routing structure
    }
  })

  return NextResponse.json({ results })
}