import { NextResponse } from 'next/server'
import { generateEmbedding } from '@/lib/huggingface'
import { createServerSupabaseClient } from '@/lib/supabase-server'
import type { VectorSearchRequest, VectorSearchResult } from '@/types'

export async function POST(request: Request) {
  try {
    const body: VectorSearchRequest = await request.json()
    const { 
      query, 
      match_threshold = 0.5, 
      match_count = 5 
    } = body

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

    console.log('Generating embedding for query:', query)
    const queryEmbedding = await generateEmbedding(query)
    console.log('Embedding generated, length:', queryEmbedding.length)

    const supabase = await createServerSupabaseClient()
    
    console.log('Calling match_documents RPC...')
    const { data, error } = await supabase.rpc('match_documents', {
      query_embedding: queryEmbedding,
      match_threshold,
      match_count,
    })

    if (error) {
      console.error('Search error:', error)
      throw error
    }

    console.log('Search results:', data)
    return NextResponse.json({ 
      results: data as VectorSearchResult[] 
    }, { status: 200 })

  } catch (error: unknown) {
    console.error("Search API Error:", error);
  
    const message =
      error instanceof Error ? error.message : "Internal server error";
  
    return NextResponse.json(
      { error: message },
      { status: 500 }
    );
  }

}