PineCone is migrating free accounts to serverless type about gpt4-pdf-chatbot-langchain

[dosubot]

Hey @tonymynd! I'm a bot designed to help you with bugs, answer questions, and become a contributor. I'm here to assist you while waiting for a human maintainer. How can I assist you today?

To resolve the issue of OpenAI not being able to read your PineCone index after migrating your PineCone free account to the serverless type, ensure that your .env file is correctly updated with the new PineCone details. Specifically, verify and update the following environment variables:

PINECONE_API_KEY=your_new_pinecone_api_key
PINECONE_ENVIRONMENT=your_new_pinecone_environment
PINECONE_INDEX_NAME=your_pinecone_index_name

Make sure these values are correctly copied from your PineCone dashboard, especially after the migration. The PINECONE_ENVIRONMENT and PINECONE_API_KEY might have changed due to the migration to the serverless type.

Additionally, ensure that your Pinecone client initialization code is correctly handling these environment variables. Here is an example of how to initialize the Pinecone client:

import { Pinecone } from '@pinecone-database/pinecone';

if (!process.env.PINECONE_ENVIRONMENT || !process.env.PINECONE_API_KEY) {
  throw new Error('Pinecone environment or api key vars missing');
}

async function initPinecone() {
  try {
    const pinecone = new Pinecone({
      environment: process.env.PINECONE_ENVIRONMENT ?? '', //this is in the dashboard
      apiKey: process.env.PINECONE_API_KEY ?? '',
    });

    return pinecone;
  } catch (error) {
    console.log('error', error);
    throw new Error('Failed to initialize Pinecone Client');
  }
}

export const pinecone = await initPinecone();

Ensure that your API handler is correctly utilizing the Pinecone client and the environment variables. Here is an example of how to use the Pinecone client in an API handler:

import type { NextApiRequest, NextApiResponse } from 'next';
import type { Document } from 'langchain/document';
import { OpenAIEmbeddings } from 'langchain/embeddings/openai';
import { PineconeStore } from 'langchain/vectorstores/pinecone';
import { makeChain } from '@/utils/makechain';
import { pinecone } from '@/utils/pinecone-client';
import { PINECONE_INDEX_NAME, PINECONE_NAME_SPACE } from '@/config/pinecone';

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

  console.log('question', question);
  console.log('history', history);

  //only accept post requests
  if (req.method !== 'POST') {
    res.status(405).json({ error: 'Method not allowed' });
    return;
  }

  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', ' ');

  try {
    const index = pinecone.Index(PINECONE_INDEX_NAME);

    /* create vectorstore*/
    const vectorStore = await PineconeStore.fromExistingIndex(
      new OpenAIEmbeddings({}),
      {
        pineconeIndex: index,
        textKey: 'text',
        namespace: PINECONE_NAME_SPACE, //namespace comes from your config folder
      },
    );

    // Use a callback to get intermediate sources from the middle of the chain
    let resolveWithDocuments: (value: Document[]) => void;
    const documentPromise = new Promise<Document[]>((resolve) => {
      resolveWithDocuments = resolve;
    });
    const retriever = vectorStore.asRetriever({
      callbacks: [
        {
          handleRetrieverEnd(documents) {
            resolveWithDocuments(documents);
          },
        },
      ],
    });

    //create chain
    const chain = makeChain(retriever);
    const pastMessages = history
      .map((message: [string, string]) => {
        return [`Human: ${message[0]}`, `Assistant: ${message[1]}`].join('\n');
      })
      .join('\n');
    console.log(pastMessages);

    //Ask a question using chat history
    const response = await chain.invoke({
      question: sanitizedQuestion,
      chat_history: pastMessages,
    });

    const sourceDocuments = await documentPromise;

    console.log('response', response);
    res.status(200).json({ text: response, sourceDocuments });
  } catch (error: any) {
    console.log('error', error);
    res.status(500).json({ error: error.message || 'Something went wrong' });
  }
}

Ensure that your config/pinecone.ts file is correctly set up to export the necessary constants:

/**
 * Change the namespace to the namespace on Pinecone you'd like to store your embeddings.
 */

if (!process.env.PINECONE_INDEX_NAME) {
  throw new Error('Missing Pinecone index name in .env file');
}

const PINECONE_INDEX_NAME = process.env.PINECONE_INDEX_NAME ?? '';

const PINECONE_NAME_SPACE = 'pdf-test'; //namespace is optional for your vectors

export { PINECONE_INDEX_NAME, PINECONE_NAME_SPACE };

This code ensures that the Pinecone client is initialized with the correct environment variables and that your API handler is correctly using the Pinecone client to interact with the Pinecone index ^[1][2][3][4].

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