import type { NextApiRequest, NextApiResponse } from 'next';
import { OpenAIEmbeddings } from 'langchain/embeddings';
import { SupabaseVectorStore } from 'langchain/vectorstores';
import { openai } from '@/utils/openai-client';
import { supabaseClient } from '@/utils/supabase-client';
import { makeChain } from '@/utils/makechain';

export default async function handler(
  req: NextApiRequest,
  res: NextApiResponse,
) {
  const { question, history } = req.body;

  if (!question) {
    return res.status(400).json({ message: 'No question in the request' });
  }
  // OpenAI recommends replacing newlines with spaces for best results
  const sanitizedQuestion = question.trim().replaceAll('\n', ' ');

  /* create vectorstore*/
  const vectorStore = await SupabaseVectorStore.fromExistingIndex(
    supabaseClient,
    new OpenAIEmbeddings(),
  );

  res.writeHead(200, {
    'Content-Type': 'text/event-stream',
    'Cache-Control': 'no-cache, no-transform',
    Connection: 'keep-alive',
  });

  const sendData = (data: string) => {
    res.write(`data: ${data}\n\n`);
  };

  sendData(JSON.stringify({ data: '' }));

  const model = openai;
  // create the chain
  const chain = makeChain(vectorStore, (token: string) => {
    sendData(JSON.stringify({ data: token }));
  });

  try {
    //Ask a question
    const response = await chain.call({
      question: sanitizedQuestion,
      chat_history: history || [],
    });

    console.log('response', response);
  } catch (error) {
    console.log('error', error);
  } finally {
    sendData('[DONE]');
    res.end();
  }
}