Closed tonymynd closed 2 weeks ago
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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].
To continue talking to Dosu, mention @dosu.
I did the migration but now my project within PyCharm does not work, OpenAI is not able to read my index.